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    "colab": {
      "name": "Facies Classification - SVM.ipynb",
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    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sunyingjian/AI-in-well-logging/blob/master/Facies_Classification_SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGmup8hYXzcY"
      },
      "source": [
        "# Facies classification using Machine Learning\n",
        "\n",
        "#### Brendon Hall, [Enthought](https://www.enthought.com/)\n",
        "\n",
        "This notebook demonstrates how to train a machine learning algorithm to predict facies from well log data.  The dataset we will use comes from a class excercise from The University of Kansas on [Neural Networks and Fuzzy Systems](http://www.people.ku.edu/~gbohling/EECS833/).  This exercise is based on a consortium project to use machine learning techniques to create a reservoir model of the largest gas fields in North America, the Hugoton and Panoma Fields. For more info on the origin of the data, see [Bohling and Dubois (2003)](http://www.kgs.ku.edu/PRS/publication/2003/ofr2003-50.pdf) and [Dubois et al. (2007)](http://dx.doi.org/10.1016/j.cageo.2006.08.011). \n",
        "\n",
        "The dataset we will use is log data from nine wells that have been labeled with a facies type based on oberservation of core.  We will use this log data to train a support vector machine to classify facies types.  Support vector machines (or SVMs) are a type of supervised learning model that can be trained on data to perform classification and regression tasks.  The SVM algorithm uses the training data to fit an optimal hyperplane between the different classes (or facies, in our case).  We will use the SVM implementation in [scikit-learn](http://scikit-learn.org/stable/modules/svm.html).\n",
        "\n",
        "First we will [explore the dataset](#Exploring-the-dataset).  We will load the training data from 9 wells, and take a look at what we have to work with.  We will plot the data from a couple wells, and create cross plots to look at the variation within the data.  \n",
        "\n",
        "Next we will [condition the data set](#Conditioning-the-data-set).  We will remove the entries that have incomplete data.  The data will be scaled to have zero mean and unit variance.  We will also split the data into training and test sets.\n",
        "\n",
        "We will then be ready to [build the SVM classifier](#Building-the-SVM-classifier).  We will demonstrate how to use the cross validation set to do [model parameter selection](#Model-parameter-selection).\n",
        "\n",
        "Finally, once we have a built and tuned the classifier, we can [apply the trained model](#Applying-the-classification-model-to-new-data) to classify facies in wells which do not already have labels.  We will apply the classifier to two wells, but in principle you could apply the classifier to any number of wells that had the same log data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3uURDUIXzca"
      },
      "source": [
        "## Exploring the dataset\n",
        "\n",
        "First, we will examine the data set we will use to train the classifier.  The training data is contained in the file `facies_vectors.csv`.  The dataset consists of 5 wireline log measurements, two indicator variables and a facies label at half foot intervals.  In machine learning terminology, each log measurement is a feature vector that maps a set of 'features' (the log measurements) to a class (the facies type).  We will use the pandas library to load the data into a dataframe, which provides a convenient data structure to work with well log data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXdaaZdoXzcb",
        "outputId": "2e3f58ad-6ec0-4bae-d551-fce57644d21c"
      },
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "\n",
        "from pandas import set_option\n",
        "set_option(\"display.max_rows\", 10)\n",
        "pd.options.mode.chained_assignment = None\n",
        "\n",
        "filename = 'facies_vectors.csv'\n",
        "training_data = pd.read_csv(filename)\n",
        "training_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Facies</th>\n",
              "      <th>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2793.0</td>\n",
              "      <td>77.450</td>\n",
              "      <td>0.664</td>\n",
              "      <td>9.900</td>\n",
              "      <td>11.915</td>\n",
              "      <td>4.600</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2793.5</td>\n",
              "      <td>78.260</td>\n",
              "      <td>0.661</td>\n",
              "      <td>14.200</td>\n",
              "      <td>12.565</td>\n",
              "      <td>4.100</td>\n",
              "      <td>1</td>\n",
              "      <td>0.979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2794.0</td>\n",
              "      <td>79.050</td>\n",
              "      <td>0.658</td>\n",
              "      <td>14.800</td>\n",
              "      <td>13.050</td>\n",
              "      <td>3.600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2794.5</td>\n",
              "      <td>86.100</td>\n",
              "      <td>0.655</td>\n",
              "      <td>13.900</td>\n",
              "      <td>13.115</td>\n",
              "      <td>3.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2795.0</td>\n",
              "      <td>74.580</td>\n",
              "      <td>0.647</td>\n",
              "      <td>13.500</td>\n",
              "      <td>13.300</td>\n",
              "      <td>3.400</td>\n",
              "      <td>1</td>\n",
              "      <td>0.915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4144</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3120.5</td>\n",
              "      <td>46.719</td>\n",
              "      <td>0.947</td>\n",
              "      <td>1.828</td>\n",
              "      <td>7.254</td>\n",
              "      <td>3.617</td>\n",
              "      <td>2</td>\n",
              "      <td>0.685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4145</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3121.0</td>\n",
              "      <td>44.563</td>\n",
              "      <td>0.953</td>\n",
              "      <td>2.241</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.344</td>\n",
              "      <td>2</td>\n",
              "      <td>0.677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4146</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3121.5</td>\n",
              "      <td>49.719</td>\n",
              "      <td>0.964</td>\n",
              "      <td>2.925</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.190</td>\n",
              "      <td>2</td>\n",
              "      <td>0.669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4147</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3122.0</td>\n",
              "      <td>51.469</td>\n",
              "      <td>0.965</td>\n",
              "      <td>3.083</td>\n",
              "      <td>7.708</td>\n",
              "      <td>3.152</td>\n",
              "      <td>2</td>\n",
              "      <td>0.661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4148</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3122.5</td>\n",
              "      <td>50.031</td>\n",
              "      <td>0.970</td>\n",
              "      <td>2.609</td>\n",
              "      <td>6.668</td>\n",
              "      <td>3.295</td>\n",
              "      <td>2</td>\n",
              "      <td>0.653</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4149 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Facies Formation        Well Name   Depth      GR  ILD_log10  DeltaPHI  \\\n",
              "0          3     A1 SH        SHRIMPLIN  2793.0  77.450      0.664     9.900   \n",
              "1          3     A1 SH        SHRIMPLIN  2793.5  78.260      0.661    14.200   \n",
              "2          3     A1 SH        SHRIMPLIN  2794.0  79.050      0.658    14.800   \n",
              "3          3     A1 SH        SHRIMPLIN  2794.5  86.100      0.655    13.900   \n",
              "4          3     A1 SH        SHRIMPLIN  2795.0  74.580      0.647    13.500   \n",
              "...      ...       ...              ...     ...     ...        ...       ...   \n",
              "4144       5      C LM  CHURCHMAN BIBLE  3120.5  46.719      0.947     1.828   \n",
              "4145       5      C LM  CHURCHMAN BIBLE  3121.0  44.563      0.953     2.241   \n",
              "4146       5      C LM  CHURCHMAN BIBLE  3121.5  49.719      0.964     2.925   \n",
              "4147       5      C LM  CHURCHMAN BIBLE  3122.0  51.469      0.965     3.083   \n",
              "4148       5      C LM  CHURCHMAN BIBLE  3122.5  50.031      0.970     2.609   \n",
              "\n",
              "       PHIND     PE  NM_M  RELPOS  \n",
              "0     11.915  4.600     1   1.000  \n",
              "1     12.565  4.100     1   0.979  \n",
              "2     13.050  3.600     1   0.957  \n",
              "3     13.115  3.500     1   0.936  \n",
              "4     13.300  3.400     1   0.915  \n",
              "...      ...    ...   ...     ...  \n",
              "4144   7.254  3.617     2   0.685  \n",
              "4145   8.013  3.344     2   0.677  \n",
              "4146   8.013  3.190     2   0.669  \n",
              "4147   7.708  3.152     2   0.661  \n",
              "4148   6.668  3.295     2   0.653  \n",
              "\n",
              "[4149 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "No2ns1LcXzch"
      },
      "source": [
        "Remove a single well to use as a blind test later."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRUz_G-hXzci"
      },
      "source": [
        "blind = training_data[training_data['Well Name'] == 'SHANKLE']\n",
        "training_data = training_data[training_data['Well Name'] != 'SHANKLE']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyvcmaEDXzco"
      },
      "source": [
        "This data is from the Council Grove gas reservoir in Southwest Kansas.  The Panoma Council Grove Field is predominantly a carbonate gas reservoir encompassing 2700 square miles in Southwestern Kansas.  This dataset is from nine wells (with 4149 examples), consisting of a set of seven predictor variables and a rock facies (class) for each example vector and validation (test) data (830 examples from two wells) having the same seven predictor variables in the feature vector.  Facies are based on examination of cores from nine wells taken vertically at half-foot intervals. Predictor variables include five from wireline log measurements and two geologic constraining variables that are derived from geologic knowledge. These are essentially continuous variables sampled at a half-foot sample rate. \n",
        "\n",
        "The seven predictor variables are:\n",
        "* Five wire line log curves include [gamma ray](http://petrowiki.org/Gamma_ray_logs) (GR), [resistivity logging](http://petrowiki.org/Resistivity_and_spontaneous_%28SP%29_logging) (ILD_log10),\n",
        "[photoelectric effect](http://www.glossary.oilfield.slb.com/en/Terms/p/photoelectric_effect.aspx) (PE), [neutron-density porosity difference and average neutron-density porosity](http://petrowiki.org/Neutron_porosity_logs) (DeltaPHI and PHIND). Note, some wells do not have PE.\n",
        "* Two geologic constraining variables: nonmarine-marine indicator (NM_M) and relative position (RELPOS)\n",
        "\n",
        "The nine discrete facies (classes of rocks) are: \n",
        "1. Nonmarine sandstone\n",
        "2. Nonmarine coarse siltstone \n",
        "3. Nonmarine fine siltstone \n",
        "4. Marine siltstone and shale \n",
        "5. Mudstone (limestone)\n",
        "6. Wackestone (limestone)\n",
        "7. Dolomite\n",
        "8. Packstone-grainstone (limestone)\n",
        "9. Phylloid-algal bafflestone (limestone)\n",
        "\n",
        "These facies aren't discrete, and gradually blend into one another. Some have neighboring facies that are rather close.  Mislabeling within these neighboring facies can be expected to occur.  The following table lists the facies, their abbreviated labels and their approximate neighbors.\n",
        "\n",
        "Facies |Label| Adjacent Facies\n",
        ":---: | :---: |:--:\n",
        "1 |SS| 2\n",
        "2 |CSiS| 1,3\n",
        "3 |FSiS| 2\n",
        "4 |SiSh| 5\n",
        "5 |MS| 4,6\n",
        "6 |WS| 5,7\n",
        "7 |D| 6,8\n",
        "8 |PS| 6,7,9\n",
        "9 |BS| 7,8\n",
        "\n",
        "Let's clean up this dataset.  The 'Well Name' and 'Formation' columns can be turned into a categorical data type.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9BbFJxwXzco",
        "outputId": "bb4057e2-ee1a-4af7-e866-ec852cb569ff"
      },
      "source": [
        "training_data['Well Name'] = training_data['Well Name'].astype('category')\n",
        "training_data['Formation'] = training_data['Formation'].astype('category')\n",
        "training_data['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SHRIMPLIN, ALEXANDER D, LUKE G U, KIMZEY A, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]\n",
              "Categories (9, object): [SHRIMPLIN, ALEXANDER D, LUKE G U, KIMZEY A, ..., NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-RDyHK6Xzcs"
      },
      "source": [
        "These are the names of the 10 training wells in the Council Grove reservoir.  Data has been recruited into pseudo-well 'Recruit F9' to better represent facies 9, the Phylloid-algal bafflestone. \n",
        "\n",
        "Before we plot the well data, let's define a color map so the facies are represented by consistent color in all the plots in this tutorial.  We also create the abbreviated facies labels, and add those to the `facies_vectors` dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiT8H2V6Xzcs",
        "outputId": "d6f428bb-3a9d-471d-b5ce-343bd5c8aa0c"
      },
      "source": [
        "# 1=sandstone  2=c_siltstone   3=f_siltstone \n",
        "# 4=marine_silt_shale 5=mudstone 6=wackestone 7=dolomite\n",
        "# 8=packstone 9=bafflestone\n",
        "facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00',\n",
        "       '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']\n",
        "\n",
        "facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS',\n",
        "                 'WS', 'D','PS', 'BS']\n",
        "#facies_color_map is a dictionary that maps facies labels\n",
        "#to their respective colors\n",
        "facies_color_map = {}\n",
        "for ind, label in enumerate(facies_labels):\n",
        "    facies_color_map[label] = facies_colors[ind]\n",
        "\n",
        "def label_facies(row, labels):\n",
        "    return labels[ row['Facies'] -1]\n",
        "    \n",
        "training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)\n",
        "training_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/Users/matt/anaconda/envs/python3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
            "  RuntimeWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Facies</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3700.000000</td>\n",
              "      <td>3700.000000</td>\n",
              "      <td>3700.000000</td>\n",
              "      <td>3700.000000</td>\n",
              "      <td>3700.000000</td>\n",
              "      <td>3700.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>3700.000000</td>\n",
              "      <td>3700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.615676</td>\n",
              "      <td>2908.853378</td>\n",
              "      <td>64.873649</td>\n",
              "      <td>0.663053</td>\n",
              "      <td>4.651677</td>\n",
              "      <td>12.892826</td>\n",
              "      <td>3.805693</td>\n",
              "      <td>1.540000</td>\n",
              "      <td>0.524125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.475808</td>\n",
              "      <td>139.010855</td>\n",
              "      <td>30.817166</td>\n",
              "      <td>0.253863</td>\n",
              "      <td>5.109006</td>\n",
              "      <td>6.796219</td>\n",
              "      <td>0.894118</td>\n",
              "      <td>0.498465</td>\n",
              "      <td>0.287147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2573.500000</td>\n",
              "      <td>10.149000</td>\n",
              "      <td>-0.025949</td>\n",
              "      <td>-21.832000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2818.500000</td>\n",
              "      <td>43.778250</td>\n",
              "      <td>0.502000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>8.350000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.278000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>2939.000000</td>\n",
              "      <td>64.817000</td>\n",
              "      <td>0.645613</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>11.857500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.531000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>3015.125000</td>\n",
              "      <td>80.322500</td>\n",
              "      <td>0.823000</td>\n",
              "      <td>7.600000</td>\n",
              "      <td>15.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.772000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>3138.000000</td>\n",
              "      <td>361.150000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>19.312000</td>\n",
              "      <td>84.400000</td>\n",
              "      <td>8.094000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Facies        Depth           GR    ILD_log10     DeltaPHI  \\\n",
              "count  3700.000000  3700.000000  3700.000000  3700.000000  3700.000000   \n",
              "mean      4.615676  2908.853378    64.873649     0.663053     4.651677   \n",
              "std       2.475808   139.010855    30.817166     0.253863     5.109006   \n",
              "min       1.000000  2573.500000    10.149000    -0.025949   -21.832000   \n",
              "25%       2.000000  2818.500000    43.778250     0.502000     1.800000   \n",
              "50%       4.000000  2939.000000    64.817000     0.645613     4.400000   \n",
              "75%       7.000000  3015.125000    80.322500     0.823000     7.600000   \n",
              "max       9.000000  3138.000000   361.150000     1.800000    19.312000   \n",
              "\n",
              "             PHIND           PE         NM_M       RELPOS  \n",
              "count  3700.000000  2783.000000  3700.000000  3700.000000  \n",
              "mean     12.892826     3.805693     1.540000     0.524125  \n",
              "std       6.796219     0.894118     0.498465     0.287147  \n",
              "min       0.550000     0.200000     1.000000     0.000000  \n",
              "25%       8.350000          NaN     1.000000     0.278000  \n",
              "50%      11.857500          NaN     2.000000     0.531000  \n",
              "75%      15.750000          NaN     2.000000     0.772000  \n",
              "max      84.400000     8.094000     2.000000     1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-ObWFTc_QdO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_VcYWzkXzcv"
      },
      "source": [
        "This is a quick view of the statistical distribution of the input variables.  Looking at the `count` values, most values have 4149 valid values except for `PE`, which has 3232.  In this tutorial we will drop the feature vectors that don't have a valid `PE` entry."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fpgLkXGXzcw"
      },
      "source": [
        "PE_mask = training_data['PE'].notnull().values\n",
        "training_data = training_data[PE_mask]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0OdPfF_Xzcz"
      },
      "source": [
        "Let's take a look at the data from individual wells in a more familiar log plot form.  We will create plots for the five well log variables, as well as a log for facies labels.  The plots are based on the those described in Alessandro Amato del Monte's [excellent tutorial](https://github.com/seg/tutorials/tree/master/1504_Seismic_petrophysics_1)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GtDTRcBzXzcz"
      },
      "source": [
        "def make_facies_log_plot(logs, facies_colors):\n",
        "    #make sure logs are sorted by depth\n",
        "    logs = logs.sort_values(by='Depth')\n",
        "    cmap_facies = colors.ListedColormap(\n",
        "            facies_colors[0:len(facies_colors)], 'indexed')\n",
        "    \n",
        "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
        "    \n",
        "    cluster=np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
        "    \n",
        "    f, ax = plt.subplots(nrows=1, ncols=6, figsize=(8, 12))\n",
        "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
        "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
        "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
        "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
        "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
        "    im=ax[5].imshow(cluster, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    \n",
        "    divider = make_axes_locatable(ax[5])\n",
        "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
        "    cbar=plt.colorbar(im, cax=cax)\n",
        "    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', \n",
        "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
        "                                ' PS ', ' BS ']))\n",
        "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
        "    \n",
        "    for i in range(len(ax)-1):\n",
        "        ax[i].set_ylim(ztop,zbot)\n",
        "        ax[i].invert_yaxis()\n",
        "        ax[i].grid()\n",
        "        ax[i].locator_params(axis='x', nbins=3)\n",
        "    \n",
        "    ax[0].set_xlabel(\"GR\")\n",
        "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
        "    ax[1].set_xlabel(\"ILD_log10\")\n",
        "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
        "    ax[2].set_xlabel(\"DeltaPHI\")\n",
        "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
        "    ax[3].set_xlabel(\"PHIND\")\n",
        "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
        "    ax[4].set_xlabel(\"PE\")\n",
        "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
        "    ax[5].set_xlabel('Facies')\n",
        "    \n",
        "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
        "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])\n",
        "    ax[5].set_xticklabels([])\n",
        "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RXzot0nXzc2"
      },
      "source": [
        "Placing the log plotting code in a function will make it easy to plot the logs from multiples wells, and can be reused later to view the results when we apply the facies classification model to other wells.  The function was written to take a list of colors and facies labels as parameters.  \n",
        "\n",
        "We then show log plots for wells `SHRIMPLIN`.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3njlhg_Xzc3",
        "outputId": "2cca6cea-0397-4e7d-f17e-4f46964dac06"
      },
      "source": [
        "make_facies_log_plot(\n",
        "    training_data[training_data['Well Name'] == 'SHRIMPLIN'],\n",
        "    facies_colors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ljLGm4j4tXAgrV8I332hLlu3ZljOgV0w+IyODBQsWsHbtWlJSUujfvz8TJ05kwIABNG3a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E+E/x8/5CiJ1CiONCiF+FEDWKn7cXQiwUQpwQQuwQQjS6rq3Xi58/KoS4ryJCR7UaRb4pn8+2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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x11041c6d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u33Syh68Xzc5"
      },
      "source": [
        "In addition to individual wells, we can look at how the various facies are represented by the entire training set.  Let's plot a histgram of the number of training examples for each facies class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pq6XY3_oXzc6",
        "outputId": "fa6065d7-0a14-42e0-a0ec-dba47190e944"
      },
      "source": [
        "#count the number of unique entries for each facies, sort them by\n",
        "#facies number (instead of by number of entries)\n",
        "facies_counts = training_data['Facies'].value_counts().sort_index()\n",
        "#use facies labels to index each count\n",
        "facies_counts.index = facies_labels\n",
        "\n",
        "facies_counts.plot(kind='bar',color=facies_colors, \n",
        "                   title='Distribution of Training Data by Facies')\n",
        "facies_counts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SS      170\n",
              "CSiS    649\n",
              "FSiS    498\n",
              "SiSh    177\n",
              "MS      198\n",
              "WS      391\n",
              "D        81\n",
              "PS      458\n",
              "BS      161\n",
              "Name: Facies, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u338BcCNdLv8cEdeu65pq8iFubySF+5vzbWVE7FZWTa1G0kWkHdvXAQcCCyLi\nhJJrmkI62mYF8FXgfGBb0j7LqRHx24F67sHWhz5U0rD8C7wf8InCtFKWJSLenf/uUMbzdyci3pr/\nblF2LQUREc/k4feR1qZuBm6W9B8l1TQS+CfSD8sHgf8BLqjtnynZJsCWwPB8W0paC7WX7RYRrwWQ\ndC7p6q5l+z5wCuk9mw0cGBE3KP0rzgvIO94HwmAL9JbbASJpAmmt6Yl8f1/gUNLx8j+IiFJ2Fkna\nFHi+dpinpF2Ag4D5EXF5GTWlMrQ58AzpB7l4TO7GZRQUES+SvmC/lbQRKdg7JH0lIv5fGTVJOof0\n/3ifIm01/Bn4bkS03j+qLd/qw04j4oXUfV26YRExCyB/jm4AiIh7Brq+QXXYYkR8DfhP4KfAWwv9\nUUNIfell6O4kgsmUexLIb4GJsPoH73pgR+A4Sd8sqaYzSMfh3gTcHRE35fp2Jx0RUApJG0l6H/Az\n4JPAWcAvy6oHGA9sRDrKZQnp3zi23j+pbQ2vVzpb/Mncvfi62rCkJ0uqqXh44j+6THMfeiuTdFtE\nvC4Pfwd4KSI+VzuJoDathLpuL2yKfhXYOiI+mftlb65NK6GuMcD2wK2143Lz4VwbRMTCEuqZDrwG\n+A1wYeGs0VLlIyVeTeo/fwupxhWkHaOnllmb9axwNJBI3Wa1bsYBPxposHW5tKKuJxGcDOkkgpI3\n/4q/1O8Avg0QEc+VdQakpFflzc4RwOQ6r886D3TgI6Qv3yuB4ws1lbbzmPzEwB2SVpK6E58gnR39\nJsCB3sIiYmhZz+1A779WPIEH4La8xbCEdEhXrU/vFSXWdCJpR3btBKOum4fr/MSiiGi5bkdJx5PW\nyvcm9RH/idRl9hO8U9R64EDvv++TTiJYRerXr+2kmUR5ZxoCfJx0MbOJwDsLR5fsBnynpJp+LGlk\nROwLIOmjpOPQ5wPtJdXUiiYClwKfiYjS9i3Y4OM+9H6S9Gvg5Ii4vcv41wJfz1fuK6Ou8WX0Sfek\nFU8MM6uSltvcHIRGdA1zgDxu4rovZ7UragOSLiuxjqK6V8aLiC+RuoXMrB8c6P3XU590WVeAhM47\na0s5W7WOVrwynlllOND77yZJH+86UtIxwM0l1FMT3QyXqXZi2JW0yIlhZlXiPvR+yofgXU46oqUW\n4G8ENgTeGxHLSqqrp2NhSzscr9WujGdWJQ70Jsmn/L8m370zImb31N7MrNkc6GZmFeE+dDOzinCg\nm5lVhAMsEn8kAAAAD0lEQVTdzKwiHOhmZhXxv2KZ5irnjMWtAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x112a1c4a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Y2L_vivXzc8"
      },
      "source": [
        "This shows the distribution of examples by facies for the 3232 training examples in the training set.  Dolomite (facies 7) has the fewest with 141 examples.  There are also only 185 bafflestone examples.  Depending on the performance of the classifier we are going to train, we may consider getting more examples of these facies.\n",
        "\n",
        "Crossplots are a familiar tool in the geosciences to visualize how two properties vary with rock type.  This dataset contains 5 log variables, and scatter matrix can help to quickly visualize the variation between the all the variables in the dataset.  We can employ the very useful [Seaborn library](https://stanford.edu/~mwaskom/software/seaborn/) to quickly create a nice looking scatter matrix. Each pane in the plot shows the relationship between two of the variables on the x and y axis, with each point is colored according to its facies.  The same colormap is used to represent the 9 facies.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "wj3rfGZ3Xzc9",
        "outputId": "88ddd75b-dec0-46f6-aa72-cd881891578c"
      },
      "source": [
        "#save plot display settings to change back to when done plotting with seaborn\n",
        "inline_rc = dict(mpl.rcParams)\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "sns.pairplot(training_data.drop(['Well Name','Facies','Formation','Depth','NM_M','RELPOS'],axis=1),\n",
        "             hue='FaciesLabels', palette=facies_color_map,\n",
        "             hue_order=list(reversed(facies_labels)))\n",
        "\n",
        "#switch back to default matplotlib plot style\n",
        "mpl.rcParams.update(inline_rc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PN/cofXdVVtrb3U9v7L+neUj64DQ8wf6b+236hFxIyKUOqDvj8FtVUvA1enMk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\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EGHITEidCaFbJgl1v8MyPrwosv+DOTdzXlmxrTdpKOrVswlfrpvXd3xJz1QOggM1dj+vz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PRo8e81HuZp7/g+SN4mOg1Dppbuzh5FGFtPTHiby1lbaH7+T8y67CtElVl3QDvrnXY517GeG1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\n8HxCKHUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1108cb3c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7hovYnmXzdA"
      },
      "source": [
        "## Conditioning the data set\n",
        "\n",
        "Now we extract just the feature variables we need to perform the classification.  The predictor variables are the five wireline values and two geologic constraining variables. We also get a vector of the facies labels that correspond to each feature vector."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "fAfgr1VaXzdA",
        "outputId": "b6cfd090-6114-410b-d1a1-f162e62bf784"
      },
      "source": [
        "correct_facies_labels = training_data['Facies'].values\n",
        "\n",
        "feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)\n",
        "feature_vectors.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.249445</td>\n",
              "      <td>0.644637</td>\n",
              "      <td>3.754963</td>\n",
              "      <td>13.118929</td>\n",
              "      <td>3.805693</td>\n",
              "      <td>1.523895</td>\n",
              "      <td>0.523057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>31.610849</td>\n",
              "      <td>0.241922</td>\n",
              "      <td>5.045916</td>\n",
              "      <td>7.389665</td>\n",
              "      <td>0.894118</td>\n",
              "      <td>0.499518</td>\n",
              "      <td>0.287499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>13.250000</td>\n",
              "      <td>-0.025949</td>\n",
              "      <td>-21.832000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>46.081500</td>\n",
              "      <td>0.497000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>8.165000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.276500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>65.541000</td>\n",
              "      <td>0.627000</td>\n",
              "      <td>3.581000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.529000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>80.714000</td>\n",
              "      <td>0.812323</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>16.144000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.771500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>361.150000</td>\n",
              "      <td>1.480000</td>\n",
              "      <td>18.500000</td>\n",
              "      <td>84.400000</td>\n",
              "      <td>8.094000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                GR    ILD_log10     DeltaPHI        PHIND           PE  \\\n",
              "count  2783.000000  2783.000000  2783.000000  2783.000000  2783.000000   \n",
              "mean     66.249445     0.644637     3.754963    13.118929     3.805693   \n",
              "std      31.610849     0.241922     5.045916     7.389665     0.894118   \n",
              "min      13.250000    -0.025949   -21.832000     0.550000     0.200000   \n",
              "25%      46.081500     0.497000     1.300000     8.165000     3.200000   \n",
              "50%      65.541000     0.627000     3.581000    11.900000     3.600000   \n",
              "75%      80.714000     0.812323     6.500000    16.144000     4.400000   \n",
              "max     361.150000     1.480000    18.500000    84.400000     8.094000   \n",
              "\n",
              "              NM_M       RELPOS  \n",
              "count  2783.000000  2783.000000  \n",
              "mean      1.523895     0.523057  \n",
              "std       0.499518     0.287499  \n",
              "min       1.000000     0.010000  \n",
              "25%       1.000000     0.276500  \n",
              "50%       2.000000     0.529000  \n",
              "75%       2.000000     0.771500  \n",
              "max       2.000000     1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFOIn-EYXzdC"
      },
      "source": [
        "Scikit includes a [preprocessing](http://scikit-learn.org/stable/modules/preprocessing.html) module that can 'standardize' the data (giving each variable zero mean and unit variance, also called *whitening*).  Many machine learning algorithms assume features will be standard normally distributed data (ie: Gaussian with zero mean and unit variance).  The factors used to standardize the training set must be applied to any subsequent feature set that will be input to the classifier.  The `StandardScalar` class can be fit to the training set, and later used to standardize any training data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcL1YXpXXzdD"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
        "scaled_features = scaler.transform(feature_vectors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZblOjfPdXzdI",
        "outputId": "36940d4b-1cb7-4396-bf66-85626bc31961"
      },
      "source": [
        "feature_vectors"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>77.450</td>\n",
              "      <td>0.664</td>\n",
              "      <td>9.900</td>\n",
              "      <td>11.915</td>\n",
              "      <td>4.600</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>78.260</td>\n",
              "      <td>0.661</td>\n",
              "      <td>14.200</td>\n",
              "      <td>12.565</td>\n",
              "      <td>4.100</td>\n",
              "      <td>1</td>\n",
              "      <td>0.979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>79.050</td>\n",
              "      <td>0.658</td>\n",
              "      <td>14.800</td>\n",
              "      <td>13.050</td>\n",
              "      <td>3.600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>86.100</td>\n",
              "      <td>0.655</td>\n",
              "      <td>13.900</td>\n",
              "      <td>13.115</td>\n",
              "      <td>3.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>74.580</td>\n",
              "      <td>0.647</td>\n",
              "      <td>13.500</td>\n",
              "      <td>13.300</td>\n",
              "      <td>3.400</td>\n",
              "      <td>1</td>\n",
              "      <td>0.915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4144</th>\n",
              "      <td>46.719</td>\n",
              "      <td>0.947</td>\n",
              "      <td>1.828</td>\n",
              "      <td>7.254</td>\n",
              "      <td>3.617</td>\n",
              "      <td>2</td>\n",
              "      <td>0.685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4145</th>\n",
              "      <td>44.563</td>\n",
              "      <td>0.953</td>\n",
              "      <td>2.241</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.344</td>\n",
              "      <td>2</td>\n",
              "      <td>0.677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4146</th>\n",
              "      <td>49.719</td>\n",
              "      <td>0.964</td>\n",
              "      <td>2.925</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.190</td>\n",
              "      <td>2</td>\n",
              "      <td>0.669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4147</th>\n",
              "      <td>51.469</td>\n",
              "      <td>0.965</td>\n",
              "      <td>3.083</td>\n",
              "      <td>7.708</td>\n",
              "      <td>3.152</td>\n",
              "      <td>2</td>\n",
              "      <td>0.661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4148</th>\n",
              "      <td>50.031</td>\n",
              "      <td>0.970</td>\n",
              "      <td>2.609</td>\n",
              "      <td>6.668</td>\n",
              "      <td>3.295</td>\n",
              "      <td>2</td>\n",
              "      <td>0.653</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2783 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          GR  ILD_log10  DeltaPHI   PHIND     PE  NM_M  RELPOS\n",
              "0     77.450      0.664     9.900  11.915  4.600     1   1.000\n",
              "1     78.260      0.661    14.200  12.565  4.100     1   0.979\n",
              "2     79.050      0.658    14.800  13.050  3.600     1   0.957\n",
              "3     86.100      0.655    13.900  13.115  3.500     1   0.936\n",
              "4     74.580      0.647    13.500  13.300  3.400     1   0.915\n",
              "...      ...        ...       ...     ...    ...   ...     ...\n",
              "4144  46.719      0.947     1.828   7.254  3.617     2   0.685\n",
              "4145  44.563      0.953     2.241   8.013  3.344     2   0.677\n",
              "4146  49.719      0.964     2.925   8.013  3.190     2   0.669\n",
              "4147  51.469      0.965     3.083   7.708  3.152     2   0.661\n",
              "4148  50.031      0.970     2.609   6.668  3.295     2   0.653\n",
              "\n",
              "[2783 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjVYoqaCXzdL"
      },
      "source": [
        "Scikit also includes a handy function to randomly split the training data into training and test sets.  The test set contains a small subset of feature vectors that are not used to train the network.  Because we know the true facies labels for these examples, we can compare the results of the classifier to the actual facies and determine the accuracy of the model.  Let's use 20% of the data for the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_tMtizaXzdM",
        "outputId": "215c7f7c-ca5c-461c-ee10-b80a60dc3e3b"
      },
      "source": [
        "from sklearn.cross_validation import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "        scaled_features, correct_facies_labels, test_size=0.2, random_state=42)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/Users/matt/anaconda/envs/python3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
            "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtkqXezTXzdP"
      },
      "source": [
        "## Training the SVM classifier\n",
        "\n",
        "Now we use the cleaned and conditioned training set to create a facies classifier.  As mentioned above, we will use a type of machine learning model known as a [support vector machine](https://en.wikipedia.org/wiki/Support_vector_machine).  The SVM is a map of the feature vectors as points in a multi dimensional space, mapped so that examples from different facies are divided by a clear gap that is as wide as possible.  \n",
        "\n",
        "The SVM implementation in [scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) takes a number of important parameters.  First we create a classifier using the default settings.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-1X4M7BXzdQ"
      },
      "source": [
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAhW8p67XzdS"
      },
      "source": [
        "Now we can train the classifier using the training set we created above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G60Q4uYrXzdT",
        "outputId": "687fa43b-f538-42f5-c0c3-93c17df46603"
      },
      "source": [
        "clf.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
              "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "  tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjPe9nG0XzdZ"
      },
      "source": [
        "Now that the model has been trained on our data, we can use it to predict the facies of the feature vectors in the test set.  Because we know the true facies labels of the vectors in the test set, we can use the results to evaluate the accuracy of the classifier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nU6Qur--Xzdb"
      },
      "source": [
        "predicted_labels = clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ZqDrJINXzdf"
      },
      "source": [
        "We need some metrics to evaluate how good our classifier is doing.  A [confusion matrix](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) is a table that can be used to describe the performance of a classification model.  [Scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html) allows us to easily create a confusion matrix by supplying the actual and predicted facies labels.\n",
        "\n",
        "The confusion matrix is simply a 2D array.  The entries of confusion matrix `C[i][j]` are equal to the number of observations predicted to have facies `j`, but are known to have facies `i`.  \n",
        "\n",
        "To simplify reading the confusion matrix, a function has been written to display the matrix along with facies labels and various error metrics.  See the file `classification_utilities.py` in this repo for the `display_cm()` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNMH7HFQXzdf",
        "outputId": "5150b5cd-4ced-4f9c-8db2-b4c23a369c70"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from classification_utilities import display_cm, display_adj_cm\n",
        "\n",
        "conf = confusion_matrix(y_test, predicted_labels)\n",
        "display_cm(conf, facies_labels, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    19    19     1                                        39\n",
            "     CSiS         102    28                                       130\n",
            "     FSiS          33    49           1                 1          84\n",
            "     SiSh           1          24           6           2          33\n",
            "       MS           2     1     2     2    42     1    10          60\n",
            "       WS                       7          43     2    19     1    72\n",
            "        D                       1                 9     7          17\n",
            "       PS                 2     1     2    22          56     5    88\n",
            "       BS                                   1          11    22    34\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdCNUW7nXzdi"
      },
      "source": [
        "The rows of the confusion matrix correspond to the actual facies labels.  The columns correspond to the labels assigned by the classifier.  For example, consider the first row. For the feature vectors in the test set that actually have label `SS`, 23 were correctly indentified as `SS`, 21 were classified as `CSiS` and 2 were classified as `FSiS`.\n",
        "\n",
        "The entries along the diagonal are the facies that have been correctly classified.  Below we define two functions that will give an overall value for how the algorithm is performing.  The accuracy is defined as the number of correct classifications divided by the total number of classifications."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rjIPQUYXzdi"
      },
      "source": [
        "def accuracy(conf):\n",
        "    total_correct = 0.\n",
        "    nb_classes = conf.shape[0]\n",
        "    for i in np.arange(0,nb_classes):\n",
        "        total_correct += conf[i][i]\n",
        "    acc = total_correct/sum(sum(conf))\n",
        "    return acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjLJkGIBXzdl"
      },
      "source": [
        "As noted above, the boundaries between the facies classes are not all sharp, and some of them blend into one another.  The error within these 'adjacent facies' can also be calculated.  We define an array to represent the facies adjacent to each other.  For facies label `i`, `adjacent_facies[i]` is an array of the adjacent facies labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nx-62jonXzdl"
      },
      "source": [
        "adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])\n",
        "\n",
        "def accuracy_adjacent(conf, adjacent_facies):\n",
        "    nb_classes = conf.shape[0]\n",
        "    total_correct = 0.\n",
        "    for i in np.arange(0,nb_classes):\n",
        "        total_correct += conf[i][i]\n",
        "        for j in adjacent_facies[i]:\n",
        "            total_correct += conf[i][j]\n",
        "    return total_correct / sum(sum(conf))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpU1AV4sXzdo",
        "outputId": "4343c7ed-b3e1-4884-8f93-4caa380a0901"
      },
      "source": [
        "print('Facies classification accuracy = %f' % accuracy(conf))\n",
        "print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf, adjacent_facies))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Facies classification accuracy = 0.585278\n",
            "Adjacent facies classification accuracy = 0.926391\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-u5oHzl_Xzdq"
      },
      "source": [
        "## Model parameter selection\n",
        "\n",
        "The classifier so far has been built with the default parameters.  However, we may be able to get improved classification results with optimal parameter choices.\n",
        "\n",
        "We will consider two parameters.  The parameter `C` is a regularization factor, and tells the classifier how much we want to avoid misclassifying training examples.  A large value of C will try to correctly classify more examples from the training set, but if `C` is too large  it may 'overfit' the data and fail to generalize when classifying new data. If `C` is too small then the model will not be good at fitting outliers and will have a large error on the training set.\n",
        "\n",
        "The SVM learning algorithm uses a kernel function to compute the distance between feature vectors.  Many kernel functions exist, but in this case we are using the radial basis function `rbf` kernel (the default).  The `gamma` parameter describes the size of the radial basis functions, which is how far away two vectors in the feature space need to be to be considered close.\n",
        "\n",
        "We will train a series of classifiers with different values for `C` and `gamma`.  Two nested loops are used to train a classifier for every possible combination of values in the ranges specified.  The classification accuracy is recorded for each combination of parameter values.  The results are shown in a series of plots, so the parameter values that give the best classification accuracy on the test set can be selected.\n",
        "\n",
        "This process is also known as 'cross validation'.  Often a separate 'cross validation' dataset will be created in addition to the training and test sets to do model selection.  For this tutorial we will just use the test set to choose model parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AZCZoivXzdr",
        "outputId": "f2dc9bab-952c-41a0-a37b-9a7874ac1dfb"
      },
      "source": [
        "#model selection takes a few minutes, change this variable\n",
        "#to true to run the parameter loop\n",
        "do_model_selection = True\n",
        "\n",
        "if do_model_selection:\n",
        "    C_range = np.array([.01, 1, 5, 10, 20, 50, 100, 1000, 5000, 10000])\n",
        "    gamma_range = np.array([0.0001, 0.001, 0.01, 0.1, 1, 10])\n",
        "    \n",
        "    fig, axes = plt.subplots(3, 2, \n",
        "                        sharex='col', sharey='row',figsize=(10,10))\n",
        "    plot_number = 0\n",
        "    for outer_ind, gamma_value in enumerate(gamma_range):\n",
        "        row = int(plot_number / 2)\n",
        "        column = int(plot_number % 2)\n",
        "        cv_errors = np.zeros(C_range.shape)\n",
        "        train_errors = np.zeros(C_range.shape)\n",
        "        for index, c_value in enumerate(C_range):\n",
        "            \n",
        "            clf = svm.SVC(C=c_value, gamma=gamma_value)\n",
        "            clf.fit(X_train,y_train)\n",
        "            \n",
        "            train_conf = confusion_matrix(y_train, clf.predict(X_train))\n",
        "            cv_conf = confusion_matrix(y_test, clf.predict(X_test))\n",
        "        \n",
        "            cv_errors[index] = accuracy(cv_conf)\n",
        "            train_errors[index] = accuracy(train_conf)\n",
        "\n",
        "        ax = axes[row, column]\n",
        "        ax.set_title('Gamma = %g'%gamma_value)\n",
        "        ax.semilogx(C_range, cv_errors, label='CV error')\n",
        "        ax.semilogx(C_range, train_errors, label='Train error')\n",
        "        plot_number += 1\n",
        "        ax.set_ylim([0.2,1])\n",
        "        \n",
        "    ax.legend(bbox_to_anchor=(1.05, 0), loc='lower left', borderaxespad=0.)\n",
        "    fig.text(0.5, 0.03, 'C value', ha='center',\n",
        "             fontsize=14)\n",
        "             \n",
        "    fig.text(0.04, 0.5, 'Classification Accuracy', va='center', \n",
        "             rotation='vertical', fontsize=14)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ydzbs3LBXgV60vGUVqzavYkvuFprWa0rz+s1pVq9Z0XOz+nuWD613KNWrVE/1j1RmYcpf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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x11770ab70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHO9LfBzXzdt"
      },
      "source": [
        "The best accuracy on the cross validation error curve was achieved for `gamma = 1`, and `C = 10`.  We can now create and train an optimized classifier based on these parameters:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjFCB8uUXzdu",
        "outputId": "53bfdfc2-9544-42c6-a1da-27a995ac53d0"
      },
      "source": [
        "clf = svm.SVC(C=10, gamma=1)        \n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "cv_conf = confusion_matrix(y_test, clf.predict(X_test))\n",
        "\n",
        "print('Optimized facies classification accuracy = %.2f' % accuracy(cv_conf))\n",
        "print('Optimized adjacent facies classification accuracy = %.2f' % accuracy_adjacent(cv_conf, adjacent_facies))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimized facies classification accuracy = 0.72\n",
            "Optimized adjacent facies classification accuracy = 0.92\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JFZk3u4Xzdw"
      },
      "source": [
        "[Precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall) are metrics that give more insight into how the classifier performs for individual facies.  Precision is the probability that given a classification result for a sample, the sample actually belongs to that class.  Recall is the probability that a sample will be correctly classified for a given class.\n",
        "\n",
        "Precision and recall can be computed easily using the confusion matrix.  The code to do so has been added to the `display_confusion_matrix()` function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1OKHKPsXzdw",
        "outputId": "ea583290-684f-44ab-f262-36e82e6b0f91"
      },
      "source": [
        "display_cm(cv_conf, facies_labels, \n",
        "           display_metrics=True, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    30     8     1                                        39\n",
            "     CSiS     2   109    17                             2         130\n",
            "     FSiS     2    23    55     3     1                            84\n",
            "     SiSh           1          27     1     2           2          33\n",
            "       MS           2     2     5    29    14           8          60\n",
            "       WS                       7     8    45     1     9     2    72\n",
            "        D                       1     2          10     3     1    17\n",
            "       PS                 3     1     3    14          63     4    88\n",
            "       BS                                               3    31    34\n",
            "\n",
            "Precision  0.88  0.76  0.71  0.61  0.66  0.60  0.91  0.70  0.82  0.72\n",
            "   Recall  0.77  0.84  0.65  0.82  0.48  0.62  0.59  0.72  0.91  0.72\n",
            "       F1  0.82  0.80  0.68  0.70  0.56  0.61  0.71  0.71  0.86  0.71\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mNDc6mqXzdz"
      },
      "source": [
        "To interpret these results, consider facies `SS`.  In our test set, if a sample was labeled `SS` the probability the sample was correct is 0.8 (precision).  If we know a sample has facies `SS`, then the probability it will be correctly labeled by the classifier is 0.78 (recall).  It is desirable to have high values for both precision and recall, but often when an algorithm is tuned to increase one, the other decreases.  The [F1 score](https://en.wikipedia.org/wiki/Precision_and_recall#F-measure) combines both to give a single measure of relevancy of the classifier results.\n",
        "\n",
        "These results can help guide intuition for how to improve the classifier results.  For example, for a sample with facies `MS` or mudstone, it is only classified correctly 57% of the time (recall).  Perhaps this could be improved by introducing more training samples.  Sample quality could also play a role.  Facies `BS` or bafflestone has the best `F1` score and relatively few training examples.  But this data was handpicked from other wells to provide training examples to identify this facies.\n",
        "\n",
        "We can also consider the classification metrics when we consider misclassifying an adjacent facies as correct:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBBec1GYXzdz",
        "outputId": "0a238f75-8f6d-4a23-c340-fcf1619fa652"
      },
      "source": [
        "display_adj_cm(cv_conf, facies_labels, adjacent_facies, \n",
        "           display_metrics=True, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    38           1                                        39\n",
            "     CSiS         128                                   2         130\n",
            "     FSiS     2          78     3     1                            84\n",
            "     SiSh           1          28           2           2          33\n",
            "       MS           2     2          48                 8          60\n",
            "       WS                       7          63                 2    72\n",
            "        D                       1     2          13           1    17\n",
            "       PS                 3     1     3                81          88\n",
            "       BS                                                    34    34\n",
            "\n",
            "Precision  0.95  0.98  0.93  0.70  0.89  0.97  1.00  0.87  0.92  0.92\n",
            "   Recall  0.97  0.98  0.93  0.85  0.80  0.88  0.76  0.92  1.00  0.92\n",
            "       F1  0.96  0.98  0.93  0.77  0.84  0.92  0.87  0.90  0.96  0.92\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c69fyecEXzd2"
      },
      "source": [
        "Considering adjacent facies, the `F1` scores for all facies types are above 0.9, except when classifying `SiSh` or marine siltstone and shale.  The classifier often misclassifies this facies (recall of 0.66), most often as wackestone. \n",
        "\n",
        "These results are comparable to those reported in Dubois et al. (2007)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ja7wllGdXzd2"
      },
      "source": [
        "## Applying the classification model to the blind data\n",
        "\n",
        "We held a well back from the training, and stored it in a dataframe called `blind`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zJN7PqnXzd3",
        "outputId": "1ca1690a-6518-4025-bbf4-4d09c2b16f03"
      },
      "source": [
        "blind"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Facies</th>\n",
              "      <th>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>937</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2774.5</td>\n",
              "      <td>98.36</td>\n",
              "      <td>0.642</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>18.685</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>938</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2775.0</td>\n",
              "      <td>97.57</td>\n",
              "      <td>0.631</td>\n",
              "      <td>7.9</td>\n",
              "      <td>16.745</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>939</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2775.5</td>\n",
              "      <td>98.41</td>\n",
              "      <td>0.615</td>\n",
              "      <td>12.8</td>\n",
              "      <td>14.105</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>940</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2776.0</td>\n",
              "      <td>85.92</td>\n",
              "      <td>0.597</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.385</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>941</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2776.5</td>\n",
              "      <td>83.16</td>\n",
              "      <td>0.592</td>\n",
              "      <td>12.3</td>\n",
              "      <td>13.345</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1381</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3006.0</td>\n",
              "      <td>32.84</td>\n",
              "      <td>1.120</td>\n",
              "      <td>-2.2</td>\n",
              "      <td>3.455</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1382</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3006.5</td>\n",
              "      <td>25.16</td>\n",
              "      <td>1.112</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>2.890</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1383</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3007.0</td>\n",
              "      <td>25.16</td>\n",
              "      <td>1.112</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>2.890</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1384</th>\n",
              "      <td>4</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3007.5</td>\n",
              "      <td>26.22</td>\n",
              "      <td>1.092</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>3.400</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1385</th>\n",
              "      <td>4</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3008.0</td>\n",
              "      <td>65.36</td>\n",
              "      <td>1.026</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4.715</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>449 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Facies Formation Well Name   Depth     GR  ILD_log10  DeltaPHI   PHIND  \\\n",
              "937        2     A1 SH   SHANKLE  2774.5  98.36      0.642      -0.1  18.685   \n",
              "938        2     A1 SH   SHANKLE  2775.0  97.57      0.631       7.9  16.745   \n",
              "939        2     A1 SH   SHANKLE  2775.5  98.41      0.615      12.8  14.105   \n",
              "940        2     A1 SH   SHANKLE  2776.0  85.92      0.597      13.0  13.385   \n",
              "941        2     A1 SH   SHANKLE  2776.5  83.16      0.592      12.3  13.345   \n",
              "...      ...       ...       ...     ...    ...        ...       ...     ...   \n",
              "1381       8      C LM   SHANKLE  3006.0  32.84      1.120      -2.2   3.455   \n",
              "1382       8      C LM   SHANKLE  3006.5  25.16      1.112      -1.6   2.890   \n",
              "1383       8      C LM   SHANKLE  3007.0  25.16      1.112      -1.6   2.890   \n",
              "1384       4      C LM   SHANKLE  3007.5  26.22      1.092      -0.4   3.400   \n",
              "1385       4      C LM   SHANKLE  3008.0  65.36      1.026       1.6   4.715   \n",
              "\n",
              "       PE  NM_M  RELPOS  \n",
              "937   2.9     1   1.000  \n",
              "938   3.2     1   0.984  \n",
              "939   3.2     1   0.968  \n",
              "940   3.4     1   0.952  \n",
              "941   3.4     1   0.935  \n",
              "...   ...   ...     ...  \n",
              "1381  5.1     2   0.060  \n",
              "1382  4.8     2   0.045  \n",
              "1383  4.8     2   0.030  \n",
              "1384  4.5     2   0.030  \n",
              "1385  4.5     2   0.015  \n",
              "\n",
              "[449 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BzvI363NXzd5"
      },
      "source": [
        "The label vector is just the `Facies` column:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ckmKQacXzd5"
      },
      "source": [
        "y_blind = blind['Facies'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pckt2pFkXzd8"
      },
      "source": [
        "We can form the feature matrix by dropping some of the columns and making a new dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epyxTulXXzd8"
      },
      "source": [
        "well_features = blind.drop(['Facies', 'Formation', 'Well Name', 'Depth'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7xYoMZVXzd-"
      },
      "source": [
        "Now we can transform this with the scaler we made before:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eXl3UVY4Xzd_"
      },
      "source": [
        "X_blind = scaler.transform(well_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ak68aQkqXzeB"
      },
      "source": [
        "Now it's a simple matter of making a prediction and storing it back in the dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7_oTx0pXzeB"
      },
      "source": [
        "y_pred = clf.predict(X_blind)\n",
        "blind['Prediction'] = y_pred"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZLURFNSXzeE"
      },
      "source": [
        "Let's see how we did with the confusion matrix:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3FDCWmTXzeF",
        "outputId": "c0d09c30-7493-4b55-a1f0-a0b43af3de3d"
      },
      "source": [
        "cv_conf = confusion_matrix(y_blind, y_pred)\n",
        "\n",
        "print('Optimized facies classification accuracy = %.2f' % accuracy(cv_conf))\n",
        "print('Optimized adjacent facies classification accuracy = %.2f' % accuracy_adjacent(cv_conf, adjacent_facies))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimized facies classification accuracy = 0.41\n",
            "Optimized adjacent facies classification accuracy = 0.87\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1aDPWHWfXzeM"
      },
      "source": [
        "We managed 0.75 using the test data, but it was from the same wells as the training data. This more reasonable test does not perform as well..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naVvhXTwXzeM",
        "outputId": "d916e5d8-12f6-4874-8b08-7ef7b3462c29"
      },
      "source": [
        "display_cm(cv_conf, facies_labels,\n",
        "           display_metrics=True, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS     6    60    23                                        89\n",
            "     CSiS    14    61    14                                        89\n",
            "     FSiS     2    51    63                             1         117\n",
            "     SiSh                       1           5           1           7\n",
            "       MS                       3     1     4           9     2    19\n",
            "       WS                 3     3    21    28          16          71\n",
            "        D                 4     1     3           2     7          17\n",
            "       PS                             1    12     2    24     1    40\n",
            "       BS                                                           0\n",
            "\n",
            "Precision  0.27  0.35  0.59  0.12  0.04  0.57  0.50  0.41  0.00  0.43\n",
            "   Recall  0.07  0.69  0.54  0.14  0.05  0.39  0.12  0.60  0.00  0.41\n",
            "       F1  0.11  0.47  0.56  0.13  0.04  0.47  0.19  0.49  0.00  0.39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQmYdOq6XzeO"
      },
      "source": [
        "...but does remarkably well on the adjacent facies predictions. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LLsRbF2XzeP",
        "outputId": "8fce1565-9ad2-4e0f-bf1f-21d1692e0ccc"
      },
      "source": [
        "display_adj_cm(cv_conf, facies_labels, adjacent_facies,\n",
        "               display_metrics=True, hide_zeros=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total\n",
            "     True\n",
            "       SS    66          23                                        89\n",
            "     CSiS          89                                              89\n",
            "     FSiS     2         114                             1         117\n",
            "     SiSh                       1           5           1           7\n",
            "       MS                             8                 9     2    19\n",
            "       WS                 3     3          65                      71\n",
            "        D                 4     1     3           9                17\n",
            "       PS                             1                39          40\n",
            "       BS                                                           0\n",
            "\n",
            "Precision  0.97  1.00  0.79  0.20  0.67  0.93  1.00  0.78  0.00  0.88\n",
            "   Recall  0.74  1.00  0.97  0.14  0.42  0.92  0.53  0.97  0.00  0.87\n",
            "       F1  0.84  1.00  0.87  0.17  0.52  0.92  0.69  0.87  0.00  0.87\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5-pgGAxXzeR"
      },
      "source": [
        "def compare_facies_plot(logs, compadre, facies_colors):\n",
        "    #make sure logs are sorted by depth\n",
        "    logs = logs.sort_values(by='Depth')\n",
        "    cmap_facies = colors.ListedColormap(\n",
        "            facies_colors[0:len(facies_colors)], 'indexed')\n",
        "    \n",
        "    ztop=logs.Depth.min(); zbot=logs.Depth.max()\n",
        "    \n",
        "    cluster1 = np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)\n",
        "    cluster2 = np.repeat(np.expand_dims(logs[compadre].values,1), 100, 1)\n",
        "    \n",
        "    f, ax = plt.subplots(nrows=1, ncols=7, figsize=(9, 12))\n",
        "    ax[0].plot(logs.GR, logs.Depth, '-g')\n",
        "    ax[1].plot(logs.ILD_log10, logs.Depth, '-')\n",
        "    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')\n",
        "    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')\n",
        "    ax[4].plot(logs.PE, logs.Depth, '-', color='black')\n",
        "    im1 = ax[5].imshow(cluster1, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    im2 = ax[6].imshow(cluster2, interpolation='none', aspect='auto',\n",
        "                    cmap=cmap_facies,vmin=1,vmax=9)\n",
        "    \n",
        "    divider = make_axes_locatable(ax[6])\n",
        "    cax = divider.append_axes(\"right\", size=\"20%\", pad=0.05)\n",
        "    cbar=plt.colorbar(im2, cax=cax)\n",
        "    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', \n",
        "                                'SiSh', ' MS ', ' WS ', ' D  ', \n",
        "                                ' PS ', ' BS ']))\n",
        "    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')\n",
        "    \n",
        "    for i in range(len(ax)-2):\n",
        "        ax[i].set_ylim(ztop,zbot)\n",
        "        ax[i].invert_yaxis()\n",
        "        ax[i].grid()\n",
        "        ax[i].locator_params(axis='x', nbins=3)\n",
        "    \n",
        "    ax[0].set_xlabel(\"GR\")\n",
        "    ax[0].set_xlim(logs.GR.min(),logs.GR.max())\n",
        "    ax[1].set_xlabel(\"ILD_log10\")\n",
        "    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())\n",
        "    ax[2].set_xlabel(\"DeltaPHI\")\n",
        "    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())\n",
        "    ax[3].set_xlabel(\"PHIND\")\n",
        "    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())\n",
        "    ax[4].set_xlabel(\"PE\")\n",
        "    ax[4].set_xlim(logs.PE.min(),logs.PE.max())\n",
        "    ax[5].set_xlabel('Facies')\n",
        "    ax[6].set_xlabel(compadre)\n",
        "    \n",
        "    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])\n",
        "    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])\n",
        "    ax[5].set_xticklabels([])\n",
        "    ax[6].set_xticklabels([])\n",
        "    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5R1jg9TvXzeT",
        "outputId": "9725bacd-f63e-4e36-d575-9c9ba09fa88b"
      },
      "source": [
        "compare_facies_plot(blind, 'Prediction', facies_colors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ttG/anqWRS8u1fnWvgcn300/Qu5iicVUDY13h4eHk5eXRoEEDvUOxnzVrCPfx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\nhpiYGE6dOlXw+8aNG0lNTWX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ng6EfcGurW20cnW1U1RqYfOHhMHGiNk/SSy+Bq2vFt2HG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\nQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1183ff470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGbjo9FcXzeX"
      },
      "source": [
        "## Applying the classification model to new data\n",
        "\n",
        "Now that we have a trained facies classification model we can use it to identify facies in wells that do not have core data.  In this case, we will apply the classifier to two wells, but we could use it on any number of wells for which we have the same set of well logs for input.\n",
        "\n",
        "This dataset is similar to the training data except it does not have facies labels.  It is loaded into a dataframe called `test_data`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6rzJ6KQXzeX"
      },
      "source": [
        "well_data = pd.read_csv('validation_data_nofacies.csv')\n",
        "well_data['Well Name'] = well_data['Well Name'].astype('category')\n",
        "well_features = well_data.drop(['Formation', 'Well Name', 'Depth'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVzAD4eTXzea"
      },
      "source": [
        "The data needs to be scaled using the same constants we used for the training data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNQMTjRWXzeb"
      },
      "source": [
        "X_unknown = scaler.transform(well_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tTx1a1crXzee"
      },
      "source": [
        "Finally we predict facies labels for the unknown data, and store the results in a `Facies` column of the `test_data` dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhIivAUrXzef",
        "outputId": "41ca7e32-fc41-4665-8442-48b83869f6ba"
      },
      "source": [
        "#predict facies of unclassified data\n",
        "y_unknown = clf.predict(X_unknown)\n",
        "well_data['Facies'] = y_unknown\n",
        "well_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "      <th>Facies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2808.0</td>\n",
              "      <td>66.276</td>\n",
              "      <td>0.630</td>\n",
              "      <td>3.300</td>\n",
              "      <td>10.650</td>\n",
              "      <td>3.591</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2808.5</td>\n",
              "      <td>77.252</td>\n",
              "      <td>0.585</td>\n",
              "      <td>6.500</td>\n",
              "      <td>11.950</td>\n",
              "      <td>3.341</td>\n",
              "      <td>1</td>\n",
              "      <td>0.978</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2809.0</td>\n",
              "      <td>82.899</td>\n",
              "      <td>0.566</td>\n",
              "      <td>9.400</td>\n",
              "      <td>13.600</td>\n",
              "      <td>3.064</td>\n",
              "      <td>1</td>\n",
              "      <td>0.956</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2809.5</td>\n",
              "      <td>80.671</td>\n",
              "      <td>0.593</td>\n",
              "      <td>9.500</td>\n",
              "      <td>13.250</td>\n",
              "      <td>2.977</td>\n",
              "      <td>1</td>\n",
              "      <td>0.933</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A1 SH</td>\n",
              "      <td>STUART</td>\n",
              "      <td>2810.0</td>\n",
              "      <td>75.971</td>\n",
              "      <td>0.638</td>\n",
              "      <td>8.700</td>\n",
              "      <td>12.350</td>\n",
              "      <td>3.020</td>\n",
              "      <td>1</td>\n",
              "      <td>0.911</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>825</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3158.5</td>\n",
              "      <td>86.078</td>\n",
              "      <td>0.554</td>\n",
              "      <td>5.040</td>\n",
              "      <td>16.150</td>\n",
              "      <td>3.161</td>\n",
              "      <td>1</td>\n",
              "      <td>0.639</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>826</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3159.0</td>\n",
              "      <td>88.855</td>\n",
              "      <td>0.539</td>\n",
              "      <td>5.560</td>\n",
              "      <td>16.750</td>\n",
              "      <td>3.118</td>\n",
              "      <td>1</td>\n",
              "      <td>0.611</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>827</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3159.5</td>\n",
              "      <td>90.490</td>\n",
              "      <td>0.530</td>\n",
              "      <td>6.360</td>\n",
              "      <td>16.780</td>\n",
              "      <td>3.168</td>\n",
              "      <td>1</td>\n",
              "      <td>0.583</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>828</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>90.975</td>\n",
              "      <td>0.522</td>\n",
              "      <td>7.035</td>\n",
              "      <td>16.995</td>\n",
              "      <td>3.154</td>\n",
              "      <td>1</td>\n",
              "      <td>0.556</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>C SH</td>\n",
              "      <td>CRAWFORD</td>\n",
              "      <td>3160.5</td>\n",
              "      <td>90.108</td>\n",
              "      <td>0.513</td>\n",
              "      <td>7.505</td>\n",
              "      <td>17.595</td>\n",
              "      <td>3.125</td>\n",
              "      <td>1</td>\n",
              "      <td>0.528</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>830 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Formation Well Name   Depth      GR  ILD_log10  DeltaPHI   PHIND     PE  \\\n",
              "0       A1 SH    STUART  2808.0  66.276      0.630     3.300  10.650  3.591   \n",
              "1       A1 SH    STUART  2808.5  77.252      0.585     6.500  11.950  3.341   \n",
              "2       A1 SH    STUART  2809.0  82.899      0.566     9.400  13.600  3.064   \n",
              "3       A1 SH    STUART  2809.5  80.671      0.593     9.500  13.250  2.977   \n",
              "4       A1 SH    STUART  2810.0  75.971      0.638     8.700  12.350  3.020   \n",
              "..        ...       ...     ...     ...        ...       ...     ...    ...   \n",
              "825      C SH  CRAWFORD  3158.5  86.078      0.554     5.040  16.150  3.161   \n",
              "826      C SH  CRAWFORD  3159.0  88.855      0.539     5.560  16.750  3.118   \n",
              "827      C SH  CRAWFORD  3159.5  90.490      0.530     6.360  16.780  3.168   \n",
              "828      C SH  CRAWFORD  3160.0  90.975      0.522     7.035  16.995  3.154   \n",
              "829      C SH  CRAWFORD  3160.5  90.108      0.513     7.505  17.595  3.125   \n",
              "\n",
              "     NM_M  RELPOS  Facies  \n",
              "0       1   1.000       3  \n",
              "1       1   0.978       3  \n",
              "2       1   0.956       3  \n",
              "3       1   0.933       3  \n",
              "4       1   0.911       1  \n",
              "..    ...     ...     ...  \n",
              "825     1   0.639       3  \n",
              "826     1   0.611       3  \n",
              "827     1   0.583       3  \n",
              "828     1   0.556       3  \n",
              "829     1   0.528       3  \n",
              "\n",
              "[830 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-QlZERKXzei",
        "outputId": "6b5b58b5-5e7d-4330-9d94-2b7b7b560a73"
      },
      "source": [
        "well_data['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[STUART, CRAWFORD]\n",
              "Categories (2, object): [STUART, CRAWFORD]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KBBSGNqUXzek"
      },
      "source": [
        "We can use the well log plot to view the classification results along with the well logs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUwZVbIQXzek",
        "outputId": "1571cb02-16c6-40f7-ab14-6ad20e8bb2ca"
      },
      "source": [
        "make_facies_log_plot(\n",
        "    well_data[well_data['Well Name'] == 'STUART'],\n",
        "    facies_colors=facies_colors)\n",
        "\n",
        "make_facies_log_plot(\n",
        "    well_data[well_data['Well Name'] == 'CRAWFORD'],\n",
        "    facies_colors=facies_colors)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f/5+jSRO5zcNkXL16lYkTJxIeHs6XX35Jly5dcHZ21luWohjgsKvrAdYdWceL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XkPD83G5desWUO+fPkIDg5WW4pOLsDqluvvp99nauRUvt3xLV18uzCu2Tjy2ljOZxm1llLT02Ha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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x114dc74a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bNmXmzJk0bdqUYsWK6S3HctSsCceOGcLJW4MSJUqwbds2AgICuHfvHmPHjsXJ\nyeEHWx2ZiUBboAiwHaglpTwvhHADtgFZcvLqCXoK4UfCeaP+G/zS6xeuj77Osp7LqFqiKn+c+YNW\ni1vh9qUbXVd05bO/P2PnxZ3EP4zXW7LJWDKW9sILEBQEx4+bXoYtxPqsobFAgQI0a9aMKVOmkJxs\nW5srZcs+CQmQP795y8wGejxvZcuWZceOHaxfv55OnTpx48YNi2pRMXlDkyylPC2lPABckFKeB5BS\nXgeyPMNJOfls4CScqOdej+GNhvPzcz8TPjKcw4MO06dmHyJjIxm9ZTQlvyhJnzV9uB53XW+5hqJI\nEXjvPXj/fb2V2AeNGjUiV65cBAcH6y3FMiQnw4ED4OurtxKr4+XlxY4dO6hTpw5+fn5cuXJFb0kK\nfXASQhQTQhQHklP+75qS7S7LvlvF5M1M7INYPtnxCYuOLOKzVp/Rv07/bA3n20y81ATu34fKlWHp\nUmja1Hzl2rPNnkRMTAw//PADL7/8Mh4eHlm+zibstWCBllxhzx4wwAYuetls4sSJbNy4ke3bt5M3\nb94clWVNVEzeLDH5MCCZjLPlSillhazoUT15M1MobyE+b/M5S3ssZcBvAzgXpRaIp+LsDBMnwtix\nYOP+1RC4uLhQqVIljh49qrcU83LhgjbsM3u2IRy8nowbNw5XV1cmTZqktxSFlZFSekspK0gpy2fw\nypKDBzXx7qkEBQUREBCQ5fOllIRFhzFt7zTebfwuPq4+lhNnZrLbVlN4+WX48kttEl7Xrtm71hr6\ncoo1NS5ZsoTIyEgGDx5slfrMwVPtExsLzz2nxXXq1zdPmSZihOfNycmJWbNmUb16dfr370+FCln+\nbs8SlmhjTmLyq6PfMZ8QnTBagEn15HOIlJJTN08x9+Bc+qzpQ9lvytJkfhPcC7nzaatP9ZZnOHLl\ngs8+077Dc5ocxxGRUnLu3DkWL17M3r176dmzJwULFtRblnk4dEhz7A0awPDheqsxDF5eXrzwwguM\nHDlSbykKG0TF5LPJg6QHhFwLYe+lvQRHBLPz4k7yOOWhhXcLWpRrQfNyzfF19TV5WZ1NxEtziJQQ\nGAh9+8LAgTkvzxFsFhsby6lTpzh48CCJiYn4+/tTo0YNcpkwnG04e8XFwTffaPnqZ8zQti00GHrb\nLCEhgRo1ajBlyhSee+45s5RpSUy119kxWRu9MTI+nx801H7yarj+CUgpiYiJYO+lvY9eRyKP4Ovq\nS6Myjeh+HpFFAAAgAElEQVTg04GprafiXdRbb6k2hRAwebK2Zv6VVyBPHr0VGZNbt25x8uRJTp48\nyc2bN/Hx8SEgIABfX9N/RBqK+Hgt7v7FF1rim/37n7qtrKOSL18+li5dSpcuXbhx4wZDhgzRW5LC\nRlDD9RkQkxDDu5vexfNrT2q/V5tlR5fhVtCNT1t9SuSoSEIGh/B9p+/pV6efXTl4a65v9feH8uUh\nO1k8bWH9rTk0xsfH88MPP7Bw4UKioqJo0aIFo0aNomfPnlSqVOmRg7cFe6TnkeZ//gEfH9i9G7Zs\ngRUrTHbwtrSG3FSCgoJo0KABwcHBTJ8+naFDh3Lv3j2zlGtujGQ3herJP4aUkuXHljN6y2jaVWzH\nrld3EX4knMDAQL2l2SVz50Lz5uDpCc8/r7ca47Bnzx5Kly5Nly5d7KPHnp6LF7VZlzNnarmOFVmm\nYsWK7N27l8GDB+Pn58eyZcuoXbu23rIUBkbF5FNISk7i1XWvEhoZyuyOs2ns1djqGkD/2J+1CQ2F\nNm1g/HgYNsy0/R3szWZTp07ljTfesFjKWt3t1aIFdOkC775rnvKsgO42S4eUkiVLljBy5Eg6dOjA\nyJEjqVu3rkXqMgWT18lPDrKQIusROi7AUDF5NVyP5uAH/jaQy3cvs3vgbt0cvCNSq5Y2Yjt/vpb6\nNipKb0X68vDhQxITEylatKjeUixDSIi2Dl7Nns8RQgj69u3L6dOnqV69Ol26dCEwMJA///xTb2kK\ng+HwTj5ZJvP6+te5eOci63uvp0CeAo997kjxJb3aWrGi5uhLldKymP7vfxlvJW4L9yInGpOSktiw\nYQOenp5ZGqa3BXs8hpQEDR4Mb70Fuc0XKXSUmHxGuLq6MnbsWM6fP8/rr7/OwIEDOXDgQI7LzQk5\nKlPvbWINttWsOXBYJy+lJDQylP6/9ufs7bP83vv3/zh4hfVwdoZvv9UmWN+4AZUqwahRcPWq3sos\ny/3797ly5Qr//vsvS5cuJS4ujpdeeklvWeZHSm0mfWSk6sVbgDx58tCzZ086duzIDz/8oLcchYFw\nqJj85ZjLbD2/lS3nt7D1/FYK5S1EB98OTG45mcL5ClukzuxitNifXly6pK2s+ukn6N0bxoyBcuUy\nPtfINpNSEhMTQ1RUFFFRUdy+ffvR/6OiokhKSqJYsWIUK1aMMmXK0LhxY4tvL2pVeyUnw9q1MGmS\n5ugXLQIbnChmxGcsOTmZ0NBQtm7dytatWwkODqZq1aq88cYbvPbaaxarNyuYaq+3alhKkfX49hiG\nisnb/ez68DvhzDk4h7Un13It9hoty7ekTYU2TAycSPli5fWWp8iEMmW03CgffADTpkG9elrinM8/\n11vZ4yQmJhITE/PodefOnUf/RkVFER0djbOzM8WKFcPV1ZVixYpRqVKlR+8LFChgnzPoATZt0oZj\nnJ3hk0+gUydDDmfaGg8fPmTo0KH8+uuvuLq60rp1a9544w2WLVuGq6ur3vJyhHo6zI9dOvlkmcyW\nc1uY9c8s/g7/m761+rKw60Lqudcjl1P2MoQZIX+1tTBiW0uV0tLgjh4NtWoF0aJFAB07WqfupKSk\nDB142vcJCQkULlwYFxcXXFxcuHDhAs2aNaNixYqPnHoeC2X7MeL9AuDaNRg5Evbt036ppXHulsqV\nbq+561NJq2X8+PFERERw8OBBypYta7ZyzYWR5jIo7MzJ3753m4UhC5n9z2wK5S3EsGeGsazHMgrm\ntZPc3g5M8eIwYoQWzm3Xzrybk0kpuXPnDlevXuXKlStcuXKF69evEx8f/5gDd3FxwdXVlfLlyz96\nX6hQocd64kFBQTRs2NB84myNM2e0TEcDBsC8eVBAzXMxJ6dOnWLBggUcO3aMkiVL6i1HYQPYRUz+\n4JWDfHfgO9aeXEunSp0Y6jeURmUa2eQwqBFjf0aiYUMYN05bZp2KqTbbtm3bI8fu5OSEh4cH7u7u\neHh4UKpUKQoXLmzx+LgeWOwZk1JLetC+vU2tgc8KRvm73L9/P2+++Sb79+83a7nmxlR7vW0HMfkZ\nKiZvPk7dPMWYrWM4fPUwQ58Zyqk3T+FW0E1vWQoL8uKL8Mcfjzt5UxFC4Ofnh7u7O4ULF7bJH4WG\n4to12LlTS1GrsAhubm6cP3+eiIgIvLy89JZjdoJ6B+ktIeeMC9BbwWPYZDcl/mE8b294G//5/jT1\nasrpt07zXtP3LOLgHSm+ZPS2BgUFcesWeHiYp7wKFSpQqVIlXFxczObgrWlDw90vd3d4/XVtsl0m\nGG5dtg7lmkKqFm9vb8aMGUOPHj24f/++2co1J0aym8JGe/IRdyKYf3g+37b/llfrvqq3HIUVCQrS\nltOZg3Xr1gFQrVo1qlevTunSpVVvPqdMnarNlpw5EwoV0luNXTJ69GiCg4P54osvGD9+vN5yzMqY\nS0l6S8gxffQWkA6bjcnvv7yfLsu78H7T9xnWYBi5nWzy98p/MErsz4iEhsKzz0J4+OPb05pqs+Tk\nZK5du8a///7L8ePHAahSpQru7u64ublRokQJk/ZrNzoWf8bq1dP2hW/a1BR5hsRof5dhYWHUr1+f\nI0eOUKZMGYvUkRNMtdeSIdssJclq9JndSsXkzUEDzwbs6L+DIX8M4fuD3zOl1RS6Vu6qemJ2SkyM\nFo//5BPz7T8vhMDd3R13d3datWrFtWvXOH36NCdPnmTHjh3cuXMHV1dX3NzcHr1KlSpFkSJF1HP2\nJN54AwYP1nIVu7jorcYu8fb2pmPHjvz555+88cYbessxG+qvyvzYZEw+lcolKrPtlW183fZrxm8f\nT/OFzfn3+r9mrcOR4ktGbWt8vLYVrY9PEK+/bpk6Uh1+ixYt6NWrF2+++SZjxoyhW7du+Pj4cO/e\nPQ4cOMD8+fP57LPPmDdvHmvXriUoKIgjR44QERFBbGws27dvt4zADDDq/WLQIG0P4U6d4PTpxz6y\npRiwkeybkZaGDRtmK099VsvNKUaym8KGe/KpCCFo79uethXbMufgHAIWBTDEbwgfNPsA59zOestT\n5JBbt6BzZ20Tm379rFt3njx5HvX003Lv3j2uX7/+KEXt2bNnH6WsTR0JSE2Ek/pydXWlSJEidjn8\n/x+E0Ibrv/kGmjTRbtz48WCvO+vpRGxsLEWKFNFbhsLg2GxMPjMux1zmrQ1vcejqIUY2GsmAugMM\nk5c+Kxgt9qcn4eFaDL5LF5gyBTJbsm4km92/fz/TPPV3796lUKFCFC1alGLFilGkSBGKFStG0aJF\nKVq0qNXW5VvVXpGR2raCa9dq8ZaBA8FA+55nFSM9Y6kMGzYMX19fRowYYbE6TMVUey21g5j8yyom\nb1k8XTxZ88Ia9l7ay1d7vmLizom8Vu81RjQaQelCpfWWp8giR49Chw5aThUDfodlirOzc4a9f9DS\n5N65c4fo6OhHr3PnzhEdHU1UVBT37t3DxcXlkdNP/TFQtGhR3N3dyW3G7VmtRqlSMHeulsFo4ULo\n1k1LXzhwIPTpA6onajLVq1fnn3/+0VuGwuDYXU8+PeejzvPV7q/YeG4j+1/bT/ECxbN1vbXzV+vZ\nYzBKru47d6BmTa33/vLL/388M31G6mXlxIaJiYmP/QBIfd2+fZvbt28/Wtfv6+tLwYIFTa5L12fs\nr78ISEqCH3/UEudMnQp9++Zo4xpr5K7X+xnLqI0nT56kVatWXLx40eQfgJbKXR8YGKh68imonryF\nqVCsAt91/I4xW8bw/Krn2faK7T9E9s7o0VovPq2DdwRy585NiRIlKFGixH8+i4uL48yZM5w+fZqN\nGzfi5uZGfHw8zZs3t63Uu05O0LKllv52/34YNgzmzIFlyyCHm604GlWqVMHLy4tNmzbR0Vq7NlmY\nqWUcYM6KlbH7nnwqoZGhtPmpDVffvYqTMO6Xot49Br05cQICArR9TrK6+srRbJaYmMju3bs5cOAA\nI0aMyPZkPkPZKykJevbUnP6wYeYv30wYymZpGD9+PLly5eLjjz+2aD3ZxVR71ZocZCFF1iN0XMB/\nevJZGaFIPwJgLuy+Jw9w5NoRRm4aychGIw3t4BXafvHDh6vl1RkhpeTGjRuEh4dz9OhRWrdubfuz\n9aOj4fp1yGD0QvF0nJ2diYuL01uG+VD5J8yO3Tr5uwl3WXFsBXMPzeVq7FUG1BnA8IbDs12OUeLU\n1kDvtkoJv/8Ohw9n/Lne+rKCOTU+ePCAS5cuERERQUREBJcuXaJgwYJ4eXnh7+/P7du3zVKPNXlk\nn5gYmDYNvv0WnnsOcjDc7Gj7yaclb968REVFmb3cnJCTdfItlrUwnxCdCNVbQDrsxsnfvneb/Zf3\ns//yfvZd3sfuiN20KNeCj1p8RDufduRysvEejwNw4IDWgzdglk6rkJCQQHh4OBcuXCAsLIybN29S\nunRpvLy88PPzo3v37hQsWPDR+TaZdCQpCWbNggkTtPWR+/ZpSRAUJlGgQIEcOXmF/WOTMfmHSQ8J\nuRbCvsv7tNelfVyLvUZ9j/o09GxIQ8+G+Jf1t8ltZ40a+7MGbdtC167ZD83aqs0ePnxIREQEYWFh\nXLhwgcjISDw9PfH29sbb2xtPT0+LLJvTzV5BQfD229oSuunToVatnJVnRYz6jJ0/f56GDRsSERGB\ns7Nxkn+p/eRVTN4kHiY9ZEHIAibunEgx52I09GxIi3ItGNNkDNVKVlO9dRtm/344d05Le26vJCUl\ncfny5Uc99cuXL1OqVCm8vb0JDAzEy8uLPOZKzG8kEhK0JRPr1sFXX2kT7VTs1SxUqFABf39/Xn75\nZebNm0dRlVVQkQ6bmIWWmJzIT0d+oup3VVl5fCWreq0idEgoc7vM5bV6r1GzVE2LOXibHBI1ET3b\n+vPP2pK5J/k4W7gXaTVKKbl27RrBwcEsWbKEzz//nA0bNpCQkECTJk149913GThwIK1ataJChQrZ\ndvC2YA/CwrTd6C5dgiNHCCpRwuwO3lFz16eyYsUK3N3dqVu3Lps3byYxMdEs5ZqKkeymMHhPPiEx\ngcVHFjM1eCoehT34scuPBHgH6C1LYQE2b4b58/VWkXOklERERHDixAlOnDgBgK+vL/Xr16dnz57k\nz59fZ4VWJDlZS2XbtauW8U713i2Cs7MzM2fOpFWrVowZM4aLFy8SGBjIs88+S9u2bSlfvrzeEhU6\nYtiY/C///sI7m96hZqmafND0A5qVe3rGIHvAqLE/S3LvnhamjYqCfPmyf70RbBYTE8Pff//NiRMn\nyJ8/P1WrVqVq1aqUKlXKcNvSWs1eS5dqM+j378984wEbwQjPWFa5du0aW7duZfPmzWzevBkXFxfa\ntm1L586dCQwMJG/evBbXoGLyKib/VD4P/pyZHWbSrUo3vaUoLExUlLZBmSkO3ggcPXqUjRs3Urdu\nXfr165dhxjqHxN0dLl6E9eu13rzCKpQuXZo+ffrQp08fkpOTHz2fEyZMoHfv3rRv357u3bvTrl07\nChUqpLdchYUxrJO/FnuNWqX0n31rpLWylkavtjo5aSurnoYR78WWLVs4ffo0L7/8Mh4eHlbVaER7\nPEbLlrBhg7aNYHQ09OtnsXXZjrpO/mk4OTlRu3ZtateuzdixY7l69Srr1q1j7ty5DBgwgOrVq/P6\n66/TpUsXs/04zUlMfnWFkWbRoCvHpumt4DEMO4bWtGxT1p1cp7cMhRXIlStrTt6InDhxghdeeAEP\nDw+9pRgTPz/Yvl2bXf/333qrcXjc3d0ZPHgwmzZtIjw8nFatWrFhwwYqVqxIQEAA06dP5+LFi3rL\nVJgRw8bk91/eT+flnVnZayXNyzXXW5bVsKXYn7m4eRMqV4Zbt0y7Xi+bSSmZNGkSY8eOtUqc01zo\nYq/ffoNRo+D0adPL0BF7/7u8d+8eW7duZe3ataxfv55evXrx3XffmTyfxFR7eXZ9x6T6jMTldV8b\nKiZv2J58A88GLOi6gOdXPs/EHRNJSrbRrp7iqcTFgS2GBoUQeHp6Eh4errcU49OwoTZkrzAk+fPn\np3PnzsyfP5/z588TEhLC8OHDsZUfKYrMMWxMHqCDbwcOvnGQfr/2Y/WJ1XzT7hurL6EzUlzO0ujV\n1piYrG1IY8R7Ubt2bTZu3MjLL79MsWLFVEw+M9atAy8vFZM3EWu0UUrJkSNHWLt2LVFRUaxatYoJ\nEyZQrFixbJdpKlPdO5h8rVHow9d6S3gMw/bkU/F08WRL3y2MazaOV9e9Ss9fenIxWsWM7ImsOnkj\nUr9+fRo2bMj8+fNVLDMztm2D8eNh+XK9lSjSIaXkwIEDjBw5kgoVKtCjRw9iY2OZO3cuERER2Xbw\nCuNh2Jh8RtxPvM9Xu7/im33f8L9m/+PNBm/aXSpbe4/9ZcSxY9oE7HPnTMuXYgSbnT17lnXr1lGt\nWjVatWpl6Bi9Ve118iS0aKGlNDRIr9gUjPCMmZPY2FiWL1/O999/z+3bt3nllVfo0aMHtWrVMkte\nB1PtlZXYtdFJH1tXMfls4JzbmXHNx7F7wG7WnlxLk/lNuHL3it6yFDmkenV48EDzB7aKj48PQ4cO\nJSEhgVmzZnHo0CESEhL0lqUvcXHQqRN89plNO3h74u7du4wcOZKyZcvyxx9/MHnyZM6dO8eECROo\nXbu24RI3KXKOTTn5VHyL+/JXv7/oXKkzrRe35mb8TYvV5Uh5mPVqqxBa9tMFC558ntHvRf78+Sla\ntChdu3blzJkzTJs2jbVr13LhwgWLTGAyuj346y/w8oJXX310yJZypRvJvubQsnv3burUqcOdO3cI\nDQ3l119/xdnZGSczZyM0kt0UWZh4J4TIB+wE8qacv0pKOUEI4Q2sAIoBh4C+UspEIUReYDFQH7gJ\nvCClDE8p631gAJAIDJdSbjZVuJNw4n/N/8e9h/d47pfnCOofZGpRCgMwZAg0agT/+5/txudTKV++\nPOXLlycuLo7Q0FA2btzIw4cPadu2LVWqVNFbnvXYtQsCA/VW4bBIKTlz5gxbt25ly5Yt7Nmzh++/\n/55u3YybRXRUno/1lmB3ZCkmL4QoIKWMF0LkAoKB4cA7aA5/pRBiNhAipfxBCDEEqCmlHCqEeAHo\nLqV8UQhRDVgKPAOUAbYCvumDVtmNYyUmJ1L6y9IcGnSIskXKZvk6o2Jvsb/s0K+f1vGbNCl71xnd\nZlJKwsLC+OOPPyhRogTt27enSJEiVqk7I6xmr7feAl9fbQ95G8foz1gq169fZ9u2bWzdupWtW7eS\nlJREmzZtaN26Ne3bt8fV1dUqOky1V6EG//8dnrdMEfKVMf7WuQmXonlw6c6j97H7ww0Vk8/SEjop\nZXzKf/OlXCOBQKB3yvFFwEfAD0DXlP8DrAK+Tfl/F2CFlDIRCBNCnAEaAPty0oArd69QIE8BTt08\nZRdO3pGZNAnq1oUBA6BCBb3VmA8hBOXLl2fQoEHMmzePNWvW8GqaIWy7pWhRuHxZbxV2T2xsLGvW\nrGHx4sX8888/tGjRgjZt2jB69GgqV65sU3F2F/ee//8mCbCBBSvOgLP7/7+PxQbT2gohnIQQh4Fr\nwBbgHBAtpUxOOeUS4Jnyf08gAkBKmQTcEUK4pj2ewuU015jEvkv7aDyvMe80fofWFVrnpKhMcaT4\nkt5t9fKCsWNh8GDIqAOkt76skJnG+Ph4fvnlF/Lly8dzzz1n0boMw0svwaJFkGYCoorJm0ZGWnbu\n3Enfvn0pU6YMK1euZNCgQVy7do1169bx5ptvUqVKlac6eFu6HwrTyGpPPhmoK4RwAdYCVTM6LeXf\njJ4q+YTj/6F///54e3sDULRoUerUqfMoYUNQUBAJiQkEEcS8w/MYXmo4de7XefQwpz5gac/PyfuQ\nkBCzlpf+/TfffENISMij9prK02yWFU2pmLuN2Xk/ciQsWhRE9+6wZk0ATk7/1Wckm2XFhjExMYwf\nPx5PT09GjRqFk5OTWWwWEhKSpfODgoJYuHAhQI5sZpK9vLxg/36C0m1OoOczlpX333zzDRs3bsyx\nw7LEMxYQEMBPP/3EyJEj6d27N6dPn8bNzY2goCD27t1rkWfoae/TPmM5QWToJhQ5Idvr5IUQHwLx\nwBigtJQyWQjRCPhIStleCLEx5f/7UmL4V6WUbkKI9wAppZyaUs6j89KV/8Q41sErB+m9ujd1Stdh\nRvsZlC5UOlv6jY6txP4syZ070L491KkD33339LXzRrbZrVu3WLJkCX5+fvj7+1u8vqxgVXu99JJ2\nM/v2zf61BsJIz9imTZvo168ff/31F9WqVTN7+eZA5a63oZi8EKIE8FBKeUcIkR9oDXwGbAd6AT8D\n/YDULeN+S3m/L+Xzv9IcXyqEmIY2TO8D7M+O2OM3jtNhWQdmtJvBCzVeyM6lChuiSBHYtAmaNoXZ\ns2HoUL0VmcaFCxdYvXo1LVu2pF69enrL0YeoKNvcmMDAzJ07l6lTpxrWwecE1wZd9JaQYy6vs720\ntu7AdiFECJrj3iSl/BN4D3hHCHEacAXmpZw/DyiRMrFuRMp5SCmPA78Ax4E/gaHZ+Zl75/4d2i9t\nzxdtvrCqg3ek+JKR2lq4MKxeDR9/DEePaseMpC8zUjWePXuW1atX07NnT4s5eMPbIzkZgoOhWbNH\nhyyh2VJ2MJJ902o5ffq02fIu2NL9UJjGU3vyUsqjwH++paSUF4CGGRxPAJ7PpKwpwJTsy4RFRxbR\nqEwjXqn9iimXK2wQHx945x2YNg3mz9dbTfY4fvw4zZs3p3z58npL0Q8pITYWihfXW4ld8cUXXzB0\n6FDWrl3LV199hY+Pj96SzIYtrQSwFWwmd73fHD++aPMFgeXtO7mGkWJ/RuDmTW05XWQk5M+f8TlG\ntNm8efNo3bo15cqVs1gdpmJVe+XODffva//aMEZ7xhISEpg+fTqff/45b775Jh988IGh9ksw1V61\nPt1hKUlWI/SDFoaKydtMWtvIuEh8XO3nF6sia5QoAR4e2uY1tkR8fDyFVCwa8ubVNiZQmJV8+fIx\nZswYjhw5wqFDh6hfvz779uUo5YjCTrEZJ5+UnISTsL5cR4ovGbWtlSrBmTPG1ZeWVI1SSpKTk598\nspnqMixJSVpcPs0QrC3FgI1k38y0eHp6sm7dOsaNG0f37t3p3Lkzu3btynLM3pbuh8I0bMbJe7p4\ncvmuyp7liPj6wunTeqvIHmXKlCE8PFxvGfpy5gx4emYeZ1GYBSEEL774IufPn6dz584MGDAAf39/\nVqxYwc2bltu8S2Eb2ExMfugfQylZoCQTAifooMp6GC32ZwR++AEOHIAff8z4cyPaLDQ0lNDQUPr0\n6WOxOkzFavaaPFmLs9jarMkMMOIzlhlJSUn8+uuvzJs3j+DgYLy9vQkICCAgIIAWLVpYJX+9ismr\nmHy2GdloJLP/mU3sg1i9pSisjC325KtVq8bVq1eJjo7WW4o+xMfDt99qyyMUViVXrlz07NmTP//8\nk1u3bjFnzhw8PDz44Ycf8Pb2pk6dOnzwwQfExMToLVWRDYQQeYQQdYUQbtm5zmacvG9xX1p4t+DH\nQ5l05yyEI8WXjNpWT0+4etW4+tKSqjE5OZnk5GRyW3BWuaHtMWUKBARAjRqPHbalGLCR7Guqlty5\nc9OwYUPGjh3Lxo0buXXrFrNnzyYyMpIaNWowZYpJK5qfiJHsZssIIb4XQlRP+X8R4AjaNu6HhRC9\nn3hxGmxqXctY/7H0/KUnbzd8W5dJeAp9cHHRUt3aCg8fPmTr1q14eXk55gz7sDCYNQuOHNFbiSId\nefLkoXHjxjRu3Jh58+bx2muvUbBgQd62g+2A7ZBmUsrBKf9/FTgtpewmhCgNbACWZ6UQm/KUfh5+\n5M2Vl9DIUKvVmboZgyNg1LaePQvlyxtXX1rKli3L999/T1xcHF26WDZFp2HtMWkSDBkCZcr85yNL\naLaUHYxkX3NpkVKya9cuevTowdixYxkzZgwvvGDeDKJGspuNk3btaRvgVwAp5bXsFGJTPXmA9j7t\n+e3Ub9QpXUdvKQor8dNP0KSJ3iqezp49e9i7dy8dO3akUqVKesvRh8hIWLPG9hIb2DmXL19m2bJl\nLF68mPv37zNixAgWL17smCNNtkO0EKITcAXwBwYCCCFyA1lesmJTPXmAvrX6sujIIpKlZdcgp+JI\n8SUjtnXuXNi5U8thb0R9qRw+fJh9+/bh6+trNQdvSHusWwfPPgvFimX4sYrJm4YpWuLi4liyZAlt\n27alZs2anD59mu+++45Tp04xbNgwChUqZFP3wwEZBLwJzAdGpOnBtwL+yGohNteT9/Pwo2SBksw5\nOIfBfoOffoHCZlm0CD78EIKCtJ3pjMzhw4dp0KABDxw9u9vu3dCqld4qHJakpCSCgoJYvHgx69at\nw9/fn4EDB7Ju3Tryq3wFNoWU8jTQTghRQkp5M83xTcCmrJZjM+vk03Ly5kmazm/Kry/+StOyTa2k\nzDrY0npcSzJjBnz5JWzeDFWqPPlcI9js+vXrLFq0iPbt21O2bFlcXFzMVra5sai9unaFfv2gRw9T\n5RkSIzxjT+LOnTtMnTqVn376iZIlS/LKK6/Qu3dvSpUqZZX602OqvZZkYT250emTbr27qevkhRCd\n0XrxD4Fk4Hkp5e7s6rG5njxAlRJV+LHLj7y0+iWqlazG+Obj8S/rr7cshRmQEiZOhCVLYNcuMOD+\nLhni5uZG586dOXz4MBs2bCBXrlyUKVPm0cvd3Z08efLoLdPyNGkCX3wBHTqAs7PeahyCw4cP06tX\nL1q0aMGGDRuokW7ZosJmmYw2w/6kEKIh8DnQIruF2KSTB+hWpRsdfDuwKGQRfdf2pVzRcrxa51W6\nVu5KEWfzje0GBQU5zGxRvdualKTlTgkK0hx8+k6I3vqeRpUqVbh27RovvvgiUVFRXLp0iUuXLvHv\nv/9y/fp1SpYsiaenJx4eHnh4eFCyZEmcnEyfFmNIe4weDQcPQq9e2nBMuq12LaHZUnYwkn0z0pKc\nnLlQjB4AACAASURBVMysWbOYMGECM2bMoHfvLC+dfmK5OSUnMfnPy+QynxDbJ1FKeRJASrlPCFHY\nlEJs1skD5M2Vl9frv07/Ov1ZfWI1y48t560NbxHoHciLNV6kU6VOFMqrZo/aAjEx0Ls3JCRoTj6T\neVs2gRACV1dXXF1dqVWrFqCtnb969SpXrlzhwoULBAcHExMTQ6lSpR45fQ8PD4oXL54jx687Tk7a\nZIrJk8HPT3P248aBl5feyuyKsLAwBg4cSFxcHH///TeVK1fWW5LC/LgJId7J7L2U8uusFGKTMfkn\nEX0/ml9P/srP//7M7ojd9K7Rm3cbv4tvcV8zqrQcRo/9WYLz56FLF2jeHKZPh+yOatuqze7fv8+1\na9e4cuXKo1dcXBwVK1akVq1a+Pj4WCRjntXsdfMmfPUVzJkD3bvDyJFQvXr2yjAIRnnGpJTMnTuX\ncePGMXr0aN555x2LZlU0FZW73iwx+Y+edL6UMksbudidk0/L9bjrzNw/k9n/zCbAO4Cx/mPx8/Az\nS9mWwihfJtZi1y54/nmts/fmm6aVYU82i4+P58SJExw9epTr169TtWpVatasSbly5RDCPHtXWN1e\nN2/C99/Dd99BrVpaTKZt28e2oDU6RnjGIiIieO2117h16xaLFi2iuoF/MCknrzaosQpuBd34JPAT\nLgy/QFOvprRf2p4Dlw9kqwxHWvNp7bYuWAA9e2qju1lx8LZwL3KqsUCBAtSvX5/+/fszaNAgXF1d\nWbVqFWFhYWavy2qUKAH/+x+EhRFUty68+y40a6ZtLWgGHGGd/JQpU/Dz86NZs2bs2bPHbA5erZO3\nf4w3zmMBCuUtxPBGw8mTKw+f7PyE9b3X6y3JoUlMhPffh19/1RLdPG2JnKNSpEgRmjRpQnh4OHFx\ncXrLyTn58kG7dlq8fuFCbbldmzbabHy3bG2s5TAkJyczbtw4fvzxR9atW0cTW0j9qDAUdj1cn56J\nOyay5fwWdr660yLlmwMjDAtaklOn4JVXtOQ2y5dD8eI5L9MebRYXF0doaCiHDx8mKSmJF198kZIl\nS5qlbMPY6+5dLZXh2rXwxx9Qtap5yzcjethMSsnw4cM5ePAgv/76q9nuvzVQw/XGGa53iJ48wOIj\ni5l3eB57Bu7RW4pDkpysbUz28ccwYYK2f4ktTyK3BPfv3+fcuXMcP36c8+fPU7lyZTp27EjZsmXN\nFo83FIULaxPzatXStqVdu9Y2NimwElOmTGHXrl0EBQVRxOgpHxWGxSG+Zrec28LoLaPZ8PIG3Au7\nZ+taR4ovWaqtERFaOvMlS7Ssp8OGmebgbeFeZEejlJIbN24QHBzMwoULmTZtGkeOHKFChQoMHz6c\nbt26PXHCnS3YIz0Zau7XTxu+79EDTp82T5lmQE/7njt3jq+++oo//viDIkWK2FQbbfG5tGfsuief\nLJP56chPjN4ymlXPr6JqSeMOB9or69fDwIEwfDiMHQsGXO1jNaSU3L59m/DwcMLDwwkLCyM5ORlf\nX1+aNGlC+fLlHSMrXka0b69tUdulC5w4YVMz7y3Bxx9/zMiRI/Hw8NBbisLGsduY/M6LOxm5aSR5\nc+Xlm2e/oWGZhmZQZ3kMEy/NIVLC1KkwcyasXg0NLWh+o9osMTGRq1evEh4eTkREBBEREeTJkwcv\nLy+8vLwoV64cbm5uVh+KN6q9AG0P+r//Bm9vy9eVDaxps5iYGLy8vLhw4QKurq7Zvt4IqJi8islb\nlKWhS3lv23t83vpzXqzxon3GMw3MlSsweDBcvQr79oGnp96KLI+UkujoaC5dusTly5e5fPkykZGR\nFC9eHC8vL2rUqEH79u1VbPVJ3LqlxXGuXDGck7cmP/30Ey1btrRZB58TWizLdmp2wxGqt4B02J2T\nT0hMYNxf41jec7lZdqgzUv5qS5PTtkqprXkfM0abWLdypbZqyij6zElycjJhYWGPnPqlS5fIlSsX\n0dHRtG3bllatWuHu7k4+cxogHUayR1bJVPOtW9oWtS+9BI0bm6fMHKKHfWNjY5k8eTK///67VbQY\nLXe9wvzYnZM/EnmEAnkK2N0WtEYnPh4GDYLQUNiyBWrX1luR5UhISGDVqlXcvXuXihUrUrt2bTp2\n7IiLiwtBQUE0baqevWyRlAQvvAAtW8KUKQ4dj3///fdp1aoV9erV01uKLjjunbccdheTvxl/E58Z\nPkSOiiRfbsv1oiyFoeOlmXDqlLa5TNWqMHcuFChg3fqtabO4uDgWLVqEp6cnnTp1Ilcu29s1y3DP\n2JQpsGkTbNsGBrWnNWy2dOlSxo8fz6FDhyhatGi2NRoJU+31th3skjvjGIaKydvdEroSBUrwrM+z\nNJnfhNO3sr8cR5F1bt+GESPA3x8GDNCWyFnbwVubhw8f4uLiwokTJ1i9ejVHjx4lISFBb1m2zYYN\n2uYFBnXwlubChQt0796djz76iFWrVtm8g1cYC7tz8gAreq7gtbqv4T/fny3ntuSoLEeKL2WnrStX\nauloExLg+HEt97ylR1mNcC+KFi1Knz59GD58OJUqVeLo0aNMmzaNn3/+mejoaKtqNII9skuGmkuW\n1HYqSkoyX5lmwNL2ffjwIZMmTeKZZ57Bz8+PY8eOZTpMb0tttMXn0p6xu5g8aENFQ54ZQtkiZRm5\naSShQ0JxEnb5e0YXfvpJW/O+YQPUr6+3Gn3Inz8/derUoU6dOty/f5+DBw/y448/4qZysGefDz/U\nMiStWQOffgqdO9t9XP7IkSO8+uqrlCpVikOHDlG2bFm9JSnsFLuLyafl/9g777Aori4OvwMqFlD5\nVBQVFVBsiL1GAQV7bxiNscSuiZpmTNNoYk1MNMZoYk3sNZZo7GLDBiiioAg2ELFgF6XO98eoUaMC\nu7M7M7vzPs8+tN17f/fMsGfvPfeeI4oiNX6vwfdNv8ffzV+WNk2N6uKlL7FypVQpdMcOqFTJLF1m\nilpsFhsby+rVq2nXrh1ly5aVtW05UYu9XkAUpfz1X3whHcn44AOpBnHu3KbrMxvIabPff/+dL7/8\nkqlTp9KnTx+LPOKrx+TVE5O3yJn8UwRBoIBdAX0WLxN790rvvbt2qcfBqwkXFxfKlSvH3bt3lZai\nPQQB2rSRMt9t3gyzZ0slafv2lZIuuLkprdBoRFFk3LhxLF68mEOHDqn6g6BSfNBS+6cKfj4VqrSE\nF7Bo75eYlMjpG6dxd3Q3uA1rii+9aayHD0PXrlLluCpVzKfpedR8LURR5PTp02zZsgUHBwez9Klm\ne7yOTDXb2kqpbf/5Bw4dkmb4detCq1bw99+vjNtrJV69cOFC1q1bR1BQULYdvFbGaKo2dQzHop38\nJzs+oYdnD0oXLK20FE0TFCS97y5aJOUr0XmRW7dusWTJEvbt24ePjw8eHh5KS7IMypaVas1fviyd\no//2W3B3h8mT4cEDpdVli4cPHzJmzBjmzZtH0aJFlZajWgRB0PxDbVhsTP7ag2uU/6U8sR/G4mBn\nnpmVHKgtXrphA/TvLx2Pa97cJF0YjZI2u337NgsXLqRevXrUrVtXE+fm1XaPZYvgYMnJx8ZKs30z\npX411mYbNmxg5syZ7Ny50yT61Iah9ooepf2dvGWnhqgqJm+xM/mt0Vvxd/PXlINXGwsWSOlp//lH\nvQ5eSR49esTixYtp1KgRDRo00ISD1zy1aknnN729wccHbt9WWlGWiIiIoHr16krL0LFCLNbJ/xP9\nD63KtTK6HWuKLz0/1nv34NNPYfdu6X1VDajtWpw/f57ChQtTu3btZ7/Tz8m/GVk0CwJMnQrVqsHM\nmZqIV0dERFDJiN2qWhijKdvUMRyLdPLpGelsj9lOi7ItlJaiWRYsgGbNpIQ3Oq8mLi6OEtZQYk+N\nCIKUJW/mTIOT6JgLURQJDQ01ysnr6BiKRTr5kKshlMhfguIOxY1uS2tVvozh+bH+/bdUEExNqOla\npKamcvLkSTw9XzzYa06NarJHVpFVc4UKkJKCr4kyMsmlddOmTQiCQC0jlsRMda1N0a4W70tLxiKd\n/N6Le2lcprHSMjTN0aPSySWd/yKKIrt376ZUqVIUKlRIaTnWy40bkJEhPVTMlClT+O677/Q9GzqK\nYJFOPvJmJFWc5DnMbU3xpefH6uEB584pp+VVqOVa7N+/n/Pnz9O2bdv//E2Pyb8Z2TSLolQVadgw\nAk+elKfNl5BDa1paGsePH8ff37iMm3pMXsdQLNLJX3t4jaL2+llUY2jXDj78EBISlFaiLi5fvkxI\nSAjvvvsueS295J5aefwY3n1X2lk/frzSat7IvXv3sLW1JV++fEpL0bFSLNLJl3AoQfz9eFnasqb4\n0vNj/fpraN0a6tUDE02Uso0arsWePXvw9fXF3t7+lX/XY/JvxmjN165JGZmSk2H7dsiVS9Xx6v/9\n738ULlyYs2fPKq7FXO1q8b60ZCzSyVcqUonjV48rLUPTCAKMHSsVBfPzg99+k1ZIrZ3Lly//Z7Od\njpkIDJTKHvr7S5WSNLKS0qNHD0aOHEm6yk8B6FgmFunkm7g2YffF3bK0ZU3xpVeNtUcPqdT37NnQ\nuTNcvGh2Wc9Qw7WwtbUlNTX1tX/XY/JvxmDNc+dC9+4wfz6MGwc2/751qT1ePW7cOFJSUhg9ejSG\nZg1U+xhN3aaO4VhkFTpPJ0/uPL7D5buXKVVAr9NsLBUqSAVqJk+WJlK9e0tHlK1xY3nlypXZvXs3\nbdq0UVqK9fDokXTDBQZqsvxhjhw5WLVqFU2aNCFnzpxMmDBBlTnOdUxHng+ycHxytmn6ttjc9QGr\nA2hVrhV9qvUxXpQZUXte8YQEqU7IypUwejQMHw65cpml69diTps9fvyYOXPm4OrqipeXF6VLl8bG\nRlsLYmq/x/7DqlXSDH7bNmX6Rx6b3bx5Ez8/P/Lnz4+3tzf169enbt26FClSRHa9SqPnrn8xd/26\niMzLT3eqVEDPXZ8d/Fz92H1BniV7nX8pVgxmzYKDB2HPHqns7JYtSqsyH7lz56Z///4UKVKE7du3\nM336dLZv306CfgzBdNy+DWXKKK3CaAoXLkxQUBCff/45tra2zJgxg7Jly1K2bFneffddZs2aRUhI\nyBvDQTo62cVinXzhvIWJSowyuh1rii9lZ6zly8PmzfDTT9JRu4YNYdMm0+YlUcu1sLe3p0GDBgwa\nNIh3330XW1tb5s2bx9WrV/WYfCZkW/PTDSHOzvK1mUVM0W6+fPlo1aoV48ePZ8eOHdy6dYsNGzbg\n6+vL8ePH6dWrFw4ODnh4eNC2bVs+/vhjfvvtN6ZPn058fLzBMf3XocfkLR+LjMk/THnIqJ2j+KXl\nL0pLsXhatZJy3K9dK+3GHz1aKmzTo4fyy/jmoEiRItSqVYuQkBCcnJyMPiqlA6SmwokT0iaQ4GCY\nNAnefltpVSbB1taWypUrU7lyZfr16wdAcnIyMTExREVFERUVxdGjRzl69CgTJ07k0aNHeHh44OHh\nQfny5fHw8KBixYpUrFiR3LlzKzwaHTVicTH5aw+u0X5Fe6oVq8acNnNkVGYeNBcvfQ5RhJ07pQJh\nZ85IM/wBA8DBxNV+lbbZ9evX+fPPP3Fzc8PX15f/manGuaEoba//kJAAhw5JuzsPH4bQUChdGt55\nB0aOhDx5TNNvNlCLzW7fvv3M+UdFRXH27FkiIiKIiYmhTJkyVKlSBU9Pz2df3dzcFEmnq8fk1ROT\ntygnf/72efz+9KN31d6M9RmryR2sankzMZaQEPj+e8npDxokvVeban+RGmyWnJzM4cOHOXLkCJUq\nVcLPz488KnBOr0JRe2VkQHg47NsHQUGSc793T8q6VL++9LVOHShQwPi+ZEQN99ibSE5O5uzZs5w6\ndYrw8HDCw8M5deoUN27coFKlSnh6elKnTh3atGmDi4uLyfXoTl49Tt6iYvLf7vuWXl69+Mb3G9kc\nvDXFl+Qca82asGIFHDkCiYlSDP+TT4xLk6vma2FnZ4ePjw9eXl4IgsCcOXOIiYkxaZ9qtscz0tOl\nT3w//gjt2xNYoAB07SqlUWzRQtoxn5go7d78+mto2jTbDl5LMXlDyUyLnZ0dXl5e9OjRg0mTJvH3\n339z8eJFrl69ys8//0z9+vU5dOgQ1apVo2bNmowfP56wsDD27Nljdq065sVinHxSahLLw5cjCAJn\nb56VfYOKjmG4u8OcOdJ7emqqdMy5b1/YsAEePlRanfzkzp2b1q1b065dOzZu3Mhff/3FxYsXre9+\nvH4dAgKkZAq9ekF0tLRRY9EiiIqSktv07i19+tPgiptWyJ8/P/Xr12fgwIH8+eefXLt2jWnTpnHn\nzh06depE9+7dmTlzpvXdn1aExSzXi6LIush1bDm3he3nt2Mj2NDMrRlN3Zvi5+pHobzayNyi9mVB\nY0lIkM7Yb9wIx45Bo0ZSMZw2baBECcPaVKvNHj9+TGhoKCdPnuTx48d4eXnh5eVF4cKFTdpvZpjc\nXmFh0L79vzF1CzgHrtZ7zBhEUSQsLIwBAwbg7OzMwoULZSudrC/Xq2e53mKc/POIosjZxLNsj9nO\njvM72HdpH36ufkzxn0K5QuVkUmoaLPHN5HXcuQNbt0pH7/75R6pfP2UKeHllrx0t2CwhIYGTJ08S\nHh5O3rx5cXNzw83NjdKlS5PLzMcQTGqv9HRp9v7rr9LM3ULQwj1mKCkpKXz22Wfs2bOHEydOyNKm\n7uTV4+QtZrn+eQRBoELhCgyvO5xN3TeR8HECdUvUpf78+ozcOpLbj25nuS1rii+Ze6wFC0ono5Yu\nlWb4T4/j9e0LcXHK6zOE12ksVqwYzZo148MPP6Rt27bkyZOHoKAgpk2bxqJFi9i7dy9xrxq0AX0p\niq0tFC0KlSu/8s9aOpetJvuacoy5cuVi2rRpXLhwgRs3bsjSpo56sEgn/zJ5cubhs4afETEsgoQH\nCYzcNlJpSTovkSsXfPCBFK51dpY2WV+7prQq+bGxsaFkyZJ4e3vTu3dvPv74Yxo2bEhycjKrVq0i\nIiJCaYnG07y5tOtSRzPY2NjQqlUrevXqxfXr15WWoyMjFrlc/yYib0TSellrzo84b7I+jMGSlwWz\nw9dfS6lzd+58oeDYK7EUm0VERHDgwAEGDhxo0n5Mbq/Ll6F6dYiIkGb1FoCl3GNvIjU1lbFjx/LH\nH38wf/58WrRoYXBb+nK9epbrLTLj3etITkvm233f4uboprQUnUz45hsoWVLyFxaQtvyN3Lt3j2PH\njhEaGkrp0qURRVGTOR6eUaoUNGggfUrr1ElpNTpZJGfOnEycOBE/Pz8GDBiAl5cX06ZNw93d3Wwa\nfM56m60v0xGitIAXsIrl+kepj9h0dhPNljQjOT2ZTd03Zfm11hRfUtNYbW3B0fHFY3Zq0vc6sqox\nPT2dqKgo1q5dy+zZs0lOTua9994jICAgyw5e1fbInRtSUv7zaz0mbxjmHKOfnx8RERHUrVuXunXr\nMnXqVKPb1FEOi53JJyYl8nfU36w/u57dF3ZTw7kGAZUCGFxrMLY25k/zqJN97O3hwQOlVchHWloa\nMTExREREEBUVhZOTE5UqVaJ169aWlXf8/n2p9vukSUor0TGQ3Llz8/nnn9OrVy9q1apFkyZNqFUr\nCzXRjWSKc2uT92FqevKT0hJewKJi8kmpSayNWMuisEUExwfj7+ZP+/LtaV2utX5OXoM0bizF5ps0\nefPz1G6z27dvExQUxKlTpyhatCgVK1akUqVKOJg6qf9rMLm95syBXbtg9WpD5KkStd9jpmTkyJHE\nxMSwaVPWV0ANtdeSwTuzrU9t9Jzjr8fk5UQURQ7HHWbB8QWsjVxLfZf6DKk1hNblWpMnpzpzh+tk\nDVtb05auNTUJCQkcPHiQmJgYatasyZAhQ8ifP7/SskzP/ftSgRkdTZOamsro0aPZsGEDq830gU3T\ne1FUiqZj8ukZ6fRa34te63vh/j93Tg09xeYem+lSqYtsDt6a4ktqG2t6+os769Wm71UEBgaSnJzM\npk2bWLp0KcWKFWPEiBH4+fnJ7uBVa4+UFKkk4SvQY/KGYc4xPnz4kF9//ZXKlSsTFRVFSEhItpbq\n1WQ3HQ3P5EVRZOCmgVy5d4WwwWHkzZlXaUk6MnP3ruqKkWXKlStXmD17Nu7u7rz//vvY2dkpLcn8\nbN0KX3yhtAqdbHLt2jVmzJjB3LlzadSoEQsWLOCtt97SZ9caR7Mx+buP71L8x+Ls77ufGs41zKDM\nPFhz7O9liheXqthlVhlTDTYTRZGgoCCOHDlC+/btzXrsKLuY1F7Hjkl56y9cAAv6gKOGe8yU7Ny5\nk3fffZdOnTrx0UcfGX3/GmqvpUN2GdWvGnhntp8ek5eDArkLMMV/CkM3D2V55+W4OroqLUlHRu7d\nkx6GFq0xN5s3b+bKlSv069ePAlpbfpCL5GQpbeGECRbl4C2dSZMmMXPmTJYuXUqTzHa56mgOTcfk\nh9Yeim8ZX2rPrU3zJc1ZG7GW1PRUWfuwpviSmsZ69ix4eGgnJn/79m1cXV05fvy42fpUlT1u3gR/\nf2nZpXfv1z5Nj8kbhqm0jBs3jnnz5hEcHCybg1eT3XQ07uRtBBsm+08m7qM4enn14uejP+Pykwuf\n7/ycmFsxSsvTMYKzZ6VS41qhc+fOREZGEhERYX21ue/ckYoNNGwo1RHOLA+xjiqIjo5m+vTpLFu2\njOLFiystR8dEaDYm/zrO3DzD3JC5/HnyT6oVq8bAGgNpX6E9uWzNW87TUCw99pdVxo2DtDT49tvM\nn6sWmyUmJrJq1SqKFi1KmzZtzF5CNqvIbq8ff5Ri8cuXyyFPlajlHpOLK1eu0KhRI0aPHm2SWgl6\nTF49MXmL+8hdoXAFpjWfRuyHsbxX7T1+Df6V0tNLMzdkLukZ6UrL08kiZ8+CiveuvZJChQrRv39/\ncuTIwaxZs9i5cydXr161/Jn9woUwdKjSKnSyyLZt2/D29mbgwIEmL4akozwW5+SfkjtHbrpX6c6e\n3nv4u/vf/BH2B7Xm1mLfpX3Zasea4ktqGasowt690urv86hF35s4ePAg7dq1o3v37gCsWrWKX375\nhd27d8tSq/t5VGGPtDSpPnDt2ll6uh6TNww5tFy8eJGOHTsybNgwZsyYwejRozV1PXQMw2Kd/PPU\nLF6T/X33M/qt0bRY0oKHKQ8zf5GOYkRHS2Fdrc3kn6dYsWL4+/szfPhwOnfuzKNHj/jtt9/I0HIK\nv1eRnCwVo4mMVFqJzhuIjY2lfv361KpVi1OnTtGmTRulJemYCYuLyWeG/UR7rn58FQc7ZfKGZ4al\nxf4MYfFi2LQJVq3K2vO1YLOwsDAiIiKezfCVRHZ7rVkDH30Ehw9LyQ0sEC3cY68jJSUFHx8fOnbs\nyKhRo8zSpx6T12PyipEuppPDRrPpAayCI0egbl2lVcjLyZMnqVKlitIyTEOXLjB4sJQEJylJaTU6\nLzFq1CicnJz45JNPlJaiowBW5+TTMtKy5eStKb6klrG+zsmrRd+beJXG+/fvEx8fT3mZzwSqyh6f\nfw4VK0pn5N8QktBSDFhN9jVUy+rVq9m4cSOLFi3C5hVHG7V0PXQMwyqdvF5PXr08fgynT0MNy8lU\nzKlTp6hQoQI5c+ZUWorpEASYOxfi42HsWKXV6ABnz55l6NChrFmzBkdHR6Xl6CiEVcXkM8QMcozP\nQcZY9W5+0nLsTw4OH5ZOY4WGZv01arfZ77//jr+/P25ubmbpLzNMaq/r16VlmO++g3feMVSi6lD7\nPfYySUlJ1K1blw8++ECRY3J6TF6+mLwgCFWACk9+jBRF8VR29FhVcDq7S/U65sfS4vE3btzg/v37\nlClTRmkp5sHJSdo12aQJuLlB/fpKK7I6RFFkyJAhVK9enQEDBigtR8dABEEoAGwAXICTgABUEQTh\nMtBeFMV7WWnHqpbr0zPSs71Ub03xJTWM9U1OXg36MuNljeHh4VSpUuWV8VC5+1INnp7wxx/QuTNc\nuvTCn7QUA1aTfbOjZf78+YSEhDB79uxMy8Rq6XpYId8CwUA5URQ7iqLYASgHHAMmZLURq3Ly+kxe\n/Rw5AnXqKK1CHkRRfObkrY6WLeGzz6BNG6mcoI5ZCA0N5fPPP2ft2rXky5dPaTk6xuEPjBZF8Vl8\n+cn3Xzz5W5awqpj87Ue3cZ3hyp3Rd0zSvhxoLfYnJzduQNmycPt29mqcqNVmly9f5u+//2bIkCGZ\nzqjMidnsJYowZAjExcGGDWCr3Q2var3Hnuf27dvUqlWLiRMn0q1bN7P1+yr0mLzxMXlBEE6Ioljt\nVc99099exqpm8voZeXVz9KiUHdVSipg9PRuvJgdvVgQBZs6Ujkx8+qnSaiwaURTp06cPrVu3VtzB\n68hGbkEQqguCUOOlR03ALquNWMjbadYwZLnemuJLSo81s013SuvLCk81pqenExERYdKlei3Yg5w5\nYfVq2LIF5s7VVAxYTfbNTMsPP/zAtWvX+OGHH2Rt1xCMaXPtsT+ePSKunJBPlAmJuHLiBd0ychX4\nEZj20uMHICGrjVjVtFY/I69ujhyBYcOUViEP0dHRFClShIIFCyotRXkcHaUd940awahR4OurtCKL\nYt++fUybNo2jR4+qtrxxVpnS+PRzP50GliklJcvUKQ88l+dqXbA87Yqi2FiOdqwqJn/pziW8F3lz\naeSlzJ+sEFqI/ZmCjAwoVAjOnIGiRbP3WjXabPXq1bi5uVGzZk2T9WEoitkrMBC6dYP9+8HDw7i2\nzIwa7zGAhIQEatasyfz582nRooVJ+8oOhtrr3Kfq+3/JLuW+D9Fz1yuFvrtevZw7BwUKZN/Bq5HH\njx8TExNDpUqVlJaiLnx9YcIEacf9rVtKq9E8aWlp9OjRg/79+6vKwRuDIGj/oTasysmni+nYCvo5\n+deh5FiPHs08CY4WrkVgYCCRkZG4urqSJ08ek/elNQLLloW2baFrV0hNladNK43Jjx07FltbXWse\nOwAAIABJREFUW8aMGSNru8aiJrvpWGFMXp/JqxNLynQXHh5OrVq1lJahXqZOhQ4d4P33Yc4cdU5/\nVM7mzZv5888/CQ0NxVbDRxNf5sj9KUpLkIEsH2E3C1Y1kzfEyfta0SYhJceaFSevhWtRo0YNrl69\niocZYs5asMfL+Pr6Suflly2TChXMmCFPmyZATfZ9XsvFixd57733WLFiBUWKFJGtXblQk910rGwm\nb0haWx3T87TyXPXqSisxnvDwcCpWrEiOHFb1r5V9HBxg40Ypt325ctC6tdKKNEFycjJdu3Zl9OjR\nvPXWW0rLkZ0pJfX/G7nRZ/KZYE3xJaXGevw4VKgAefO++XlauBZr167Fy8vLLH1pwR4v84Lm0qVh\n3Tro2xfCw+VpU0bUZN+nWj766CNKlSrFyJEjZW1XTtRkNx0rm8mnZaRle+OdjumxlHj8tWvXSE5O\npnTp0kpL0Q716klL9u3aSTeCk5PSilTLsmXL2L59O8HBwZabRdFSx6UgVuXkDUlra03xJaXGeuQI\nNG+e+fPUfi3Cw8Pp0KGD2d6A1W6PV/FKzd27SwkSOnSA3bshd27j25QBNdnXycmJrl27snPnTgoU\nKCBbu3pM3vLRl+t1FMcSZvJPK86Za6ne4hg7FlxcoH9/qbCNzjMePHhAly5dmDp1KlWrVlVajo7G\nsConr9eTfzNKjPXGDSkvSvnymT9Xzdfi0qVL5M6dm8jISLP1qWZ7vI7XaraxgUWLICpKSpgjR5tG\nogb7iqLIwIEDKVOmDH379pW9fT0mb/lYlZPXZ/Lqw1Iqz508eVKfxRtLnjxSSdrff5eK2ugwe/Zs\nTp8+zYgRI5SWoqNRrMrj6efk34wSY83OUr1ar0VaWhpnzpxh0KBBssZLM0Ot9ngTmWp2dpaO1jVr\nBq6ukIWkQpYakz969Chjx44lKCiIcuXKmaQPPSZv+Wh8/pQ9DElrq2NaLCEef+7cOYoWLWpWB2/R\nVKsGc+dKG/Hi4pRWowiJiYkEBATw22+/mczB61gHVuXk9XPyb8bcY83IkJbr69TJ2vPVei1Onjz5\nrG68OTWq1R5vIsua27eH4cOlo3UPH8rTZjZRyr4ZGRn06tWLzp0706lTJ5Nq0WPylo9VOfn0jOwf\nodMxHZZQee7Ro0dcuHBBrzhnCj79VJrV9+wpfSK0EiZPnsydO3eYPHmy0lJ0LACrqie/PHw5G6M2\nsrzzcpO0LwdqrVttCv78EzZvhpUrjWtHSZuFhIQQExNDQECA0W2ZC03dYykp4O8Pb70FkyaZv/8n\nmMtmu3fv5p133iE4OJgSJUpk67VqwlB7eU3caypJZuPkFz56PXml0HfXqwtLiMfrZ+NNTK5cUurb\n1avhjz+UVmNS4uPj6dmzJ4sXL9a0g9dRF1bl8QytJ28tu0XNPdYjR6BHj6w/X23X4u7du1y/fp2y\nZcs++505NarNHlnBIM2FC8OmTeDrC25u0KiR8W1mAXPaNzU1lW7dujFkyBD8/f9bqlRLYzQmJj8q\nNk0+IQrRU2kBL6HP5HUU4dEjiIiAGjWUVmI4esU5M1KxIixeDAEBcP680mpk58svv8Te3p4vv/xS\naSk6FoZVxeTnBM/hRMIJ5rSZY5L25UBT8VIjOHQIhg2D0FDj21LCZqIoMnv2bFq3bq25gjSavsdm\nzZIeQUFQsKDZujWlzTZs2MDw4cMJDQ2lUKFCBmtUE4baa+mQXaaSZDbeme2nx+SVYs/FPbg5uikt\nQwdpV/2VK1Kq8itXlFaTfURRJDExUY+dmpthw6BlS2jcGBISlFYjC1OmTOHXX3+1GAevoy6sxslv\njd7K4bjDvF/n/Wy9zprOfJpzrJUqwdmzUrjVy0uqT5Ka+ubXqOla2NjYkD9/fu7cufPC7/Vz8m9G\nFs0//CDdNA0awPXrmjpD/irOnDlDcnIyKSkpZtein5O3fKzCyc8NmUuvv3rxR4c/yJszr9JydJ5Q\nsCBMngzHj0ub8Jo1g+vXlVaVNU6ePAmAvb29wkqshMRE6azle+9J1er27oWmTbVf9AD49NNPmTx5\nMkWKFKFt27bMnDmTs2fPoorwiI7msdiYfGp6KjvP72TBiQWEXwtnY/eNeBTykEGhadF0vNQI0tNh\nzBhYsgR27ACPbFwqc9vszp07/PLLL5QvX56KFSvi5OREoUKFsLXVRspkVd9joigtw0dESI/TpyEk\nRKpO5+0NzZtLnwbLlQNB9vDlazGHzRITE9m1axfbtm1j+/bt2NjY4OvrS7169ahXrx5VqlTRzCZP\nQ+3lPLxR5k9UOVd/3q+qmLxFOfn0jHT2X97P8vDlrDuzjnL/K8fbnm/Tp1of8tvll1mpaVD1G7AZ\nWLAAxo2Dffsgq/vZzG2zjIwMzpw5w/Xr15897t69i6OjI05OTjg5OVGkSBGcnJxwdHTERmWzTVXc\nY6II8fH/OvKnTj0iAmxtoXJlKaZTqRJUrSolVMiVS77+s4m5bSaKIpGRkRw4cIDDhw9z5MgRLl26\nRI0aNahbty716tWjbt26lCxZ0qD2TY3u5HUn/1oM+cd4kPKAuSFz+enwTxTKW4i3K79NN89ulClY\nxmg95j6LrOQbsFrOXc+YAb/+CocPg6Pjv79/nT41OK20tDRu3rzJ5s2bKVWqFGFhYTx8+JA8efLw\n4YcfkjNnTtn6eoqh18vs9nr8WHLeJ04Q+Pff+CYmQlgY2Nm96Myffl+kSLaaN8cZcqXvscDAQKpX\nr86xY8eeOf3Dhw+TL18+WrVqRevWrWnSpAl58uTJdrumOCffuHFjg+y1ZMhOWbUoQc/Z/qpy8tpY\n+3kNiUmJzDgyg9nBs/Et48vagLXULlFbaVk6RjJiBFy+DF26wNatYAL/KDs5cuR4tuP+7t272Nvb\nU7duXTw9PU3i4FXLgwfS+ciwMDhxQvoaEwNly0p56IsWlXbIe3ll25lbOwUKFMDf3/9ZshxRFImI\niGDLli18//33dO/enUaNGtG6dWs6depEsWLFFFacfQTMF4KxFjQ9k++wogO5c+RmfOPxmoi3ZwWl\nZwxqIT0dOnYEe3spB8qbwt1qsFlGRgbffvst3t7eeHp6UkTFDswk9srIgEWL4KuvJIdeo4bk1KtW\nlWbndnZGqlYWNdxjmXHnzh2mTJnC5MmTGTp0KLNmzTJb3y+jn5PXZ/JG8zDlIbsv7ObSyEs45nHM\n/AU6msLWVtpM3aYNDBoE8+YprejN2NjYkCdPHurUqUO+fPmUlmNewsOhb1/JkW/cCLVqKa3I6nj8\n+DFfffUVq1evZt68efTt21dpSToqQV07grKBiEh+u/zsubjHpP1Y05lPtY01Tx6YOlVasgf16XsZ\nOzs7du0y30xENfb4+Wfw84MDBzJ18Fo6l60a+5K5lo8//piLFy8SGRlJv379srzZU0vXQ8cwNDuT\nt89lz/q319NyaUtsBBval2+PYMYjNTrmYfFi6N1baRVZIzU1VTPH6GQlOVlaktf//xRh1apVbN26\nldDQUAoUKKC0HB2VoemYPMDei3sZtmUYhfMW5odmP1CruLaXCrUQ+zMX8fFQpQqcPAlvyh6rBpsl\nJiaycOFCPvroI9UdmXsZ2e01eLAUex8yRA55qkQN99ir2LVrF927d2fbtm1Ur17dpH1lBz0mr8fk\nZcOnjA8nBp9g4fGFtFvejrYebfmpxU96ZjuNI4rw6adSgjMtpIePjo7GxcVF9Q7eJJQoAX/9BR06\ngLOz0mqsghs3bjBnzhxmzpzJmjVrVOXgdf7LN+tPKNa3Rbwj5bDJwYCaAzjz/hmS0pKo9Xstwq+F\ny9K2NcWX1DTWH36QjlZ/882/v1OTvpepVKkSV69eZcGCBWbrUzX2GDkSqlcHT0+YMEGqI/watBQD\nVo19+VfL6dOnGTBgAB4eHly+fJm9e/fi7e1tdLtyoia76ViIk39Kfrv8LO64mNENR9PkzyayOXod\n85GeDuPHS3u5Nm4ErWxUt7e3x9PTk+DgYOvLOe7gAFOmwNGjUu3g4sWha1dYuNBiKsUpxcOHD9my\nZQu//PILlSpVwt/fn1KlShEVFcXcuXOpWLGi0hJ1VI7mY/KvY8nJJYzbO47gAcEUyK2dzShqjf2Z\ng8RE6N4dUlJg+fKsr/wqaTNRFLl37x579uwhPj6egIAAChcubHS7psTk9kpIkI5EbNkiFSJwd4dW\nraBJEynnvLOz5grLmPMeO3fuHGvWrGHHjh0cO3aMmjVr0qxZM5o2bUqNGjU0sblTj8m/GJP3mrg3\n09ed/MJHj8lnh55ePZlycAohV0No4tpEaTk6WeDYMSln/eHD6g3tpqWlER8fT1xcHHFxccTGxiKK\nIhUqVKB///7kUjC/umooVgz69JEeqalSBrwtW+CLL+D8ebh7VypM4Ob24sPVVfrq4KD0CBTj7Nmz\neHt7061bNz7++GN8fHz0Soc6RmGxTj7yRiSxd2N5y+Uto9pRSz53c6D0WFu0kFZ4mzeHDRugXr0X\n/66UvgcPHnDgwAFiY2O5ceMGhQsXpmTJklSsWJGmTZtSsGDBZ8c3zalR6euVJXLmlKrHPYkbBwYG\n4lu7Nly8KDn8p4/AwH+/z5fvRadfsyY0bCilxH0F5shdbw6SkpJo27Yt7du357vvviN//n+Lamlp\njHpMXl1YnJN/lPqIqQenMvPoTKb4T8Euh7bTaVob3btLqWy7dJHCu05OSiuSluQfPHjAjRs3KFq0\nKFWrVqVChQr6DMtQ8uWTitFUrvzfv4kiXL/+r8OPjpY++fXvL90MDRtCo0bSw83Nos7mi6JIq1at\nOHr0KM7OzpQsWZJatWpRs2ZNbG1tqVmzJg5WvMqhYxgWE5NPy0hj5amVjAkcQ/Vi1fmx+Y+UKlDK\nBApNizXH5J/nyy8hMhLWrcv8ueayWWpqKtHR0URERHDu3DlKlixJjx49NHdsTpP3WEYGnDoF+/f/\n+xBFaWf/qFEm797cNktLSyMyMpLg4GBCQkIICQnh5MmTuLi44OnpSYkSJShevPh/Hvnz51dFUjA9\nJq/H5GUjNT2VpeFLmbB/AsXsizG37Vw9Bm8BDBoEbxkXaZGdnDlzUrFiRSpWrMiZM2fYu3evKt5Q\nrQIbG6lynZeXlHxnwgSpHnFty6w6mSNHDqpUqUKVKlWe5aFPTU0lMjKSyMhI4uPjuXr1KuHh4cTH\nxz97pKenU7x4cZydnV/5IeDpQ1+Fsh406eRvP7rNrgu72Ba9jS3RWyhfqDxz287Fp7SP7G+6moh7\nyoSaxlq4MNy8KU3Wnl5SNek7e/YsKSkp7NmzhzJlylCyZEly5cqlx+QzIduak5Ph7FlpFn/6tPQI\nC5M27j05rqeleLWhPNXi5eWFl5fXa593//59rl69+oLjv3r1KiEhIc9+vnLlCjly5KB06dLkz5+f\nunXr4ubmhru7O25ubpQpUwY7I6oG6jF5daEJJy+KIkevHGVr9Fa2xWzj1PVTNCzVkObuzfm4wcdU\nKFxBaYk6MpM3r+Tck5LUeVa+ZcuWXL58mUuXLhEYGEhCQgJFixblzp07lChRAldXV3Lk0MS/lzpI\nS4OoqH+d+dOvly5JG/AqV5aS7fTsCZMmQfnymjuGZw4cHBxwcHDAw+P1pbdFUeTu3btcunSJDRs2\nkDdvXk6fPs2mTZuIiYkhNjaWokWLvuD4n35fuXJl66uyqHFUH5M/df0Uw7YMI+FBAu082tG8bHMa\nlmpI7hy5FVRpOjQZLzURlSvD7NnPNma/FjXYLDU1lbi4OC5evEhUVBQuLi60atVKtvblRHF7iaK0\noe7YsX8fJ05I5yarVPl3U56nJ3h4gAqOJSpuMzOSlpZGXFwcMTExnD9//oWv0dHRtGzZkh49etC8\nefPXzvj1mLwek8+U1PRUPt/1OX+G/ck3vt8wqOYgbG3UnwRCRz5GjJAmbUZk7TQbOXPmxNXVFVdX\nVzw9PVm8eDEtW7bUY/YgpTHcuRP27pUcenCwdBa+dm3pMW6cdEyuYEGlleog7QcoU6YMZcqUwc/P\n74W/3bx5kzVr1jBt2jT69u1Lx44d+eijj6hUqZJCanUyQ7VOfmn4Ug7GHuTU0FM45VPuHJWa4nKm\nRm1j7d1bcvKBgeDrqz59r2Lp0qXcu3ePQoUKIYqiSZ286u1x7RrMnw+//y5tsmjdmsAmTfBdvFhK\nmCMT1hSTV7rdwoULM3jwYAYPHkxcXBzTp09n+PDh7Ny584U2DWVKSX0iJzeqdfKzjs1inO84RR28\njrLY2cHUqdIpqePHlVbzZtLT01m1ahWHDx9m6NChVKhQwXpn8TduwAcfwLZtUsKDNWug1pMS0IGB\nsjp4HeUoWbIkn3/+Oa6urmRkZGjuKKm1oMqYfML9BMr/Up7EUYlWt0RvTbG/rCCKUKoU7NkDZcu+\n+jlqsJkoiixcuBBXV1caN24sW7umwKT2unYN/PyktIVff20xS/BquMfUSGJiIq6urty9e/eFD7WG\n2isrsWu183JsXemYvCo/egXFBtHApYHVOXid/yIIUkx+3z6llbwZQRAICAjg+PHjnD9/Xmk5yiCK\n0LQpBATAtGkW4+B1Xk90dDQlS5a03lUrDaBKJ3/g8gEalmqotAzAus58qnWsT528WvU9xd7eniJF\nirBlyxbS09NN3p/q7BEeDg8fwpgxr32KluqXq8m+ah1jtWrVSEtLY8uWLbK1qSMvqnTyB2MPGl1Y\nRsdyaNBAqkynBVxcXMifPz+RkZFKSzE/27ZJy/Q6VoOdnR0zZszgk08+wdJDE1pFlTH5vBPycuPT\nG+TNmVdpOWZHj/39lzt3pARnd++++u9qs9m2bdtwcHCgQYMGJmnfWExmL39/acNd+/bGyFMlarvH\n1IQoilSuXJnZs2fj4+MD6DF5PSafCZWKVLJKB6/zalJTpfokWni/TEtL4/Lly+TNa2X3b1ISHDkC\nKt90qCM/giDQp08fVq9erbQUnVegSiffoKR6ZkDWFF9S61j37AEfH9i7N1BpKZkydepUChYsSNWq\nVU3el6qu1969UKMGPFcD/VXoMXnDUPsYGzZsyKFDh2RtU0ce1OnkXdTj5HWUZ9cuaSVY7cTFxXHl\nyhXat29vfbuNt22DZs2UVqGjEHfu3DGqqI2O6VBlTP7SnUuarAUvB3rs77+4u8OGDVIq81ehBpuJ\nosgff/yBl5cXNWrUkK1dUyC7vUQRKlSApUv/TXpjYajhHlMzXbp0wc/PjyFDhgB6TF6PyWdCyfwl\nlZagoxIuXJBOZVWurLSSN3Ps2DHS0tKoVq2a0lLMz+7dYGsrLdfrWB3BwcEEBQXRs2dPpaXovAJV\nOnkbQT2yrCm+pMax7t0r7eUSBHXqA57Vle/YsSP7zJi1RzX2+P13qZpQFtKa6jF5w1DrGB8+fMjg\nwYMZP348Dg4OsrSpIy/q8abPkZ5h+kQiOtogKkr9s/iHDx+SO3duChUqpLQUZThzBurUUVqFjplJ\nTU0lICAAT09P+vXrp7Qci0cQhEKCIHQUBKFmdl6nSie//NRypSU8Qy1VqMyBGsd69+6/5cTVqA/g\n/v375MuXDzCvRtXYIzYWXFyy9FRTaDaVHVRjX9Q3xqSkJLp06YKNjQ1z5859YaOpmuymZQRB+FsQ\nBM8n3zsDp4D3gMWCIIzMajuqdPIfb/+YFadWkCFmKC1FR2HatoW1a5VW8WauXLmCs7Oz0jKU4cED\nePwYrHUVwwq5ceMGTZs2xcHBgbVr15IzZ06lJVkqrqIonnryfV9ghyiKbYG6SM4+S6jSyW98eyOT\nDkzCdYYrX+3+iqjEKMW0WFN8SY1j9feXMt6tWKFOfSAlwHlaZtOcGlVhj1OnwMND2jSRBfSYvGGo\nZYx//fUXXl5e+Pj48Oeff5Lr6TKbEW3qvJbU5773A7YAiKJ4H8jyDFiVTr5uybqEDQ5j49sbeZT6\nCO+F3tSfX58lJ5fos3srI0cOWL4chg+H+Hil1byaihUrEhERwa1bt5SWYn727AF9edbiEUWR3r17\nM2rUKFavXs3EiRP1+vGmJ1YQhA8EQegI1AC2AgiCkAfI8vKJKs/Jv6wpLSONbdHbmLB/AkmpSXzf\n9HuaujdVSKFp0c/jvprp06XZ/P798PLqoBpsduDAAYKCgqhYsSLe3t4UKFBAtrblRlZ7jR0rnZMf\nP14ueapEDfeYkhw+fJhevXpx4sSJLKVs1s/JG39OXhAEJ2A84AzMEkVx+5PfNwZqiqL4Q1b0aMLJ\nP0UURdZFrmP0rtFUcarCii4ryGX73+UiLWPtbyavQxShTRuoXRu++ebFv6nFZo8ePSIoKIiQkBCq\nVauGr6/vK5czlUZWe/3xB+zYAUuWyCVPlajlHlOKYcOG4ezszFdffZWl5xtqryVDdhmkT030nO0n\nl5OvBoQZexNpar1FEAQ6V+pMxNAI0sV0Bm4aaPLyhtYUX1LzWAUBevcOZOZMuHpVaTWvJk+ePNja\n2jJs2DAePXrErFmziIyMNNk9qorrlZIC2UhnqsfkDUPJMaakpLBq1Sp69OghW5s6WWIecFMQhB2C\nIHwjCEIzQRDeXBziFWjKyT8lp21OlnVaxomEE6w/s15pOTpmwskJ+vZV/8pwvnz5aN++PR07dmT3\n7t1s3rzZcmttx8WBtZ4ssAJEUWTs2LFUrlwZNzc3k/cnWMBDLkRRrAW4ABOAFGA4cE4QhDBBEH7N\najuaWq5/mXGB40jNSOW7Jt+ZWJX5sPZlwcxITJTSpB88KG3qBnXbLDk5meXLl1OgQAHat2+vis1K\nstrL1xc++wxatpRJnTpR8z1mKlJTUxk0aBDh4eFs3rwZJyenLL/WUHsttYDl+ndkWq5/HkEQ8gH1\ngLeAXoCNKIpZ+tSVI0uqVciF2xdYf3Y9AZUClJaiY0YKFYKPPoKvvoJVq5RWkzkpKSkULFiQM2fO\ncO/ePQoWLKi0JPlYuVJKSdiokdJKdGTm1KlT9O7dm5IlS7Jnzx7s7e3N0m8d+1Fm6UcLCILQA2gA\nVAOSgWPAEaChKIoJWW1H+WmFAew8v5N68+vRt1pfRjccbdK+rCm+pPaxPtU3YoQ0kw8OVlbPq3iq\nMSUlhd27dzN79mzy5s3LiBEjZHfwil6vzZulC7F1K2TDAegxecMw5xinT59O48aNGTp0KOvXr8+2\ng1eT3TTO70iz90XAEFEUR4ui+Fd2HDxocCZ/+vppuq/tzpqua/Ap46O0HB0FyJtXms3PmgULFyqt\n5r9ER0ezefNmSpYsyaBBg1R9nM4gbt6Efv1g3Trw8lJajY6MxMXFMX78eEJDQylTpozZ+89iTiVr\noQBQFWk2/40gCOWBq8Ah4JAoiruz0oimYvJJqUlUnVOVr72/plfVXmZWZh6sMfZnCPHxUuGaa9fA\nzk4dNktOTmbLli1cvnyZ1q1bU7ZsWVnblwuj77EBAyBfPil5gZVgLf+XP/74I2vWrOGff/4x6sOp\nofaKHpWt2iuqpOzUEJPUkxcEoSjQBfgQKeWtbVb0aGom//ORn6lWrJrFOnidrFO8uDSjv3ZNaSUS\nN2/eZOXKlZQsWZIhQ4ao8ny8LMTFScUEzp9XWomOCWjTpg3BwcG4urrSo0cPhg8fjsfTHa5m4Mj9\nKWbry3T4y9KKIAheSLP4p49cSLP4mcDBrLajmZj8o9RHTDs0je8am3cnvTXFl9Q+1pf1FSggValT\nmrS0NObNm0f9+vUpUKCA2Ry8Itdr6VLo2BEM3F+gx+QNw1xj9PDwYNmyZYSHh+Po6EiDBg34/fff\njWozeyh9AE5Nh+hYBFQG/gH8RFEsJYpiN1EUZ4iimOUdSZqZye84vwNPJ0/KFy6vtBQdlZCUJM3m\nlcbW1hZ7e3uKFCnCvXv3lJZjWurXh0WLpBSEegDVYilRogTffvst27dvp3Tp0krLsUpEUawhRzua\nicl/tuMz7HPZ87XP1wqoMh/WEvszlvBw6eRWXBw4OChvs/3793P//n1atWolW5umwqh7TBShbl1p\nQ8TPP4ODg6lkqgpr/L9cvnw5o0ePJiYmhhw5sjcf1M/Jyx+TNxTNLNcXzF2Qh6kPlZahowJu35ZW\njH/9NVunt0xKuXLliI6OttzMdk8RBNi1C2xtoVo1OHJEaUU6JuCff/5h5MiRbN68OdsOXkddaMbJ\nO+Vz4tpD8++yUlNcztSofayBgYEkJ0sOvn17yGIqbbNQtGhRcufOzYwZM8zWp2LXy8EB5s2DH36A\ntm0hLCzLL9Vj8oZhzjHev3+fvn37sm7dOjw9PWVpU0c5NOPki9oX5doDlWyl1lGEpCR4910oXBim\nTlVazYsIgkCPHj2IiYkhNDRUaTnmoWNHmDlTcvTXryutRkcmvv/+e5o2bcpbb72ltBQdGdBMTH5x\n2GL+Pvc3K7usVECV+bDG2F9miKKUd2XkSPDzg9mzIU+ef/+uJptdvXqVFStWMHLkSASVbkyT3V4j\nRkBqqhQ/sVDUdI+ZkkOHDtGhQweCg4NxcXExuB09Jq+hmLwgCHaCIBwRBOG4IAjhgiCMffL7MoIg\nHBYE4awgCMsFQcjx5Pe9BUG4LghC6JPHe8+11VsQhKgnr8nWYffQq6HUdNZ+ogSdrCOKsH8/tGgB\nY8ZIp7cWLXrRwasNZ2dn8uTJw+XLl5WWYj7GjIHVq8FaVjAskNTUVJYvX05AQABz5841ysHrqItM\nd1SIopgsCEJjURSTBEGwBQ4KgrAV+AiYJoriakEQZgP9gN+evGyFKIrDn29HEARHYAxQA+kwYYgg\nCBtEUczSSefQhFC+9jD/zvrAwEB8fX3N3q8SqGWsGRmwYYO0JH/zJnzyiVRiNigoEPBVVFtmBAYG\nUqJECa5fv27yo0dquV4UKgRz5kjL9gcPwhvSoZpCs6nsoBr7YjotmzZtIjIykpkzZ+Lu7s5vv/1m\n9AkRY2Ly7ZtZQEXR2UoLeJEsbZsURTHpybd2T14jAo2B7k9+/wcwln+d/KuWHJoD258Ri/qTAAAg\nAElEQVQ6dUEQtgMtgEzX3zPEDI5fPU71YtWzIldHo9y5I83UZ82Scq189pkU9rXNUvJG9VCkSBHC\nwsLw8PCwvLz1r6NzZ7hyRSo9u2iR9FVHFaSlpXH58mViYmKIjo4mOjr62fcxMTF06dKFDRs2UKOG\nLMeydVRGlmLygiDYACGAOzAL+AEpQb7Hk7+XBLaIouglCEJvYCJwA4gCPhRF8YogCB8DdqIoTnzy\nmq+AJFEUf3ypr//Esc4lnqPp4qZcHHnRqMFqAWuJ/T3P6dPSketVq6Sl+WHD4K23sp5rRW02y8jI\n4ODBgxw+fJhmzZpRtWpVk/RjKCa116ZNMHiw5PQnTlTPGUcjUds99jKiKHL+/HkiIyNfcOLR0dHE\nxsZStGhRypYti7u7+3++mqKMrKH2eri+sexazE2+DntUFZPP6kw+A6guCEJ+4C+g4que9uTrRmCZ\nKIqpgiAMAv4E/Hj17P6V/wF9+vR5VgGpYMGC3CpwixrO0qfMp0tBT5eutP7z9OnTOXHihNEVn162\nWbVq1VQzxjf9vGIFDB4cSMeOEBHhi7Oz9Pe9e7VrMxsbG9LT03F3d+fAgQMkJiYiCAKCIChi48DA\nQBYtWgRglM2yZK+2beGttwjs3h1KlMD3gw/g/fcJPHPGbOOV42e132MAa9euJTQ0lPj4eHbu3Mnj\nx49xc3OjTp06lC1bluLFi9O9e3e6deuGnZ3dK9sLDg5W1T2mIz/Z3l0vCMIYIAkYBRQTRTFDEIR6\nwFhRFFu+9FwbIFEURUdBEN4GfEVRHPzkb3OAPaIornzpNf/59PvZjs9wsHPgK++vsjk84zF3XE7J\nGYO5xzp3LowbB//8A1WqZP781+lT0yzrZY0PHz5k8eLFlC1bFn9/eQpXvK6vrGI2e507J1WqW7ZM\nmtmPH09gVJTs95ip7tvn21X6HnuqJTIykjlz5rBr1y7i4+Np3Lgxfn5++Pv7U65cuWyf6DCF7QID\nA2ncuLE+k3+C0jP5rOyuLywIQoEn3+dBKrETAewBuj55Wm9gw5PnFHvu5e2ByCffbwOaCoJQ4Mkm\nvKZPfpcpoQmhz2byOpbBvXswahTs2ZM1B69V8uXLR0BAAMePH1daivkpV07aYHHuHPzvf1IeYrWU\nDdQooaGh/P777/zyyy/cuHGDtWvXMnToUDw8PFR7ZFNHWTKdyQuCUAVpY53Nk8dKURQnCILgCqwA\nHIHjQM8nS/QTgXZAKnALGCKKYtSTtvoAXyIt038niuKfr+jvP59+C08tTPiQcJwdnI0ZqyZQesZg\nLn77DXbsgDVrjG9L7TaLj49n/fr1DB061Cz9ZYZi9vrxR8nph4VpLlavlntMFEX69etHQkICv/zy\nC25ubrK2Lxd6TF5DM3lRFMNFUawhimI1URS9RFGc8OT3F0RRrCuKoocolb9LffL7L0RR9BRFsboo\nin5PHfyTvy0SRbHck9f8x8G/jjuP71AkXxFDxqejUjZtgoAApVWYHlEU2bt3L5UrV1ZaivJ89BHc\nugWPHimtRLMIgsCvv/5KjRo1qFOnDj169CAsG2mFdawPTaS1tbWxJS0jTZG+n24usQbMOdb9+6Fx\nNj+0a+FaPK8xJSWFHTt2cPv2bRo2bGjSvjTBrl0EpqVJeYllxFR2UJN9n9eSO3duvvvuO86fP0/1\n6tVp2bIlvr6+zJgxg4sXLxrcrlyoyW46GnHyTvmc9Lz1FsbDh+DoqLQK0yCKIqdPn2bWrFk8ePCA\nXr16Yau1w/5y8+uv8M470k5LPXYsC/nz5+fTTz/l/PnzfPjhh5w8eZI6depQtWpVvv76a4KDgy2/\nKqJOpmgid33rZa0plq8Ys1rPIneO3AopMw9qif2Zmly54MED6auxqM1mJ06cYP/+/bRr187kWe8M\nwaz2OncOPvwQLl6U0hi6u2e/DRWgtnvsdaSnp3P48GE2bNjAxo0buXXrFt7e3vj6+uLr60ulSpWw\nsTH93M5Qe33Rrcyznxt5FsS7ivpnAvvCb7P/1J1nP09ceVFVMXlNOPl7yfd4b8N7XLxzkSWdllCh\ncAWF1JkerbyZGEvu3HD3LtjZGd+W2mw2d+5cfH19KVeunEnaNxaz2OvOHZg0CebPl1IXjhghzyc6\nhVDbPZZVYmNj2bt3L4GBgQQGBnL37l18fHzw8fGhadOmlC9f3iS78vWNd+px8ppYrs9vl5/VXVfz\nrte7eC/0xneRL4vDFpOUmpT5i43EmuJL5hyrjY2Uoz47aOFa7Nmzh1u3bvG///3P5H2p0h5Xr0pO\n3d0dbtyA8HD49NNnDl5LMWA12ddQLS4uLvTs2ZN58+YRHR1NaGgoHTp0ICwsDH9/f1xcXPj000/Z\nv38/6enpimrVMQ2acPIgfTIcUW8EcR/F8X6d91l2ahklfyzJkL+HEByvx560hiCATO8pqkIQBGrV\nqsXhw4eVlmJeUlOlfMSVK0NSEoSEwIIF4Gz5x161xPNOPzY2lq+//po8efLwwQcfUKxYMfr06cM1\nPZeBRaGJ5frXEXs3lj/C/mDB8QU42DnwXrX36OnVk0J5C5lYpenQ6rJgdqlcWapjUru28W2pzWYP\nHjzg119/pX///maZ0WcXk9hr+3Zpxr5jBzg5GStRdajtHpObXbt2MWjQIIoUKcK6detwNvLDmb5c\nry/Xy4JLARe+8v6K6OHR/NT8J47GH8X9Z3e6renG9pjtZIjZXA/WMRstW0rpbC0Re3t76tWrxz//\n/ENGdmMSWmX3bvD3t0gHb2mIokhsbCwbNmzgm2++oXHjxgwcOJDJkycTFBRktIPXUReadvJPsRFs\naOLahKWdlnJhxAW8S3kzeudoXGe48k3gN1y6c8ngtq0pvmTOsfr4wIED2XuNFq7FU40NGjQgPT2d\nv//+22ShJFXZo0sXWLw404uqx+QNw1AtGRkZnDt3jpUrV/LZZ5/RrFkzihQpQu3atfntt9+IiYlh\n+PDhREZG0qVLF1k24anJbjoW4uSfxzGPI8PqDCN0UCjru60nMSmRmr/XxGeRD78c/YX4+/FKS9QB\nqlaFkyeVVmE6cuTIwdtvv82NGzeYP38+QUFB3L59W2lZpqNWLVi6FNq3h/ffhxMnlFZklYiiyKlT\np5g+fTpt27bF0dGRZs2asXLlShwcHBgxYgTh4eEkJCSwZcsW+vXrR8eOHcml4ZMPOm9G0zH5rPI4\n7TE7YnawJnINm85uolKRSnSt1JWulbtS3KG4rH0Zi6XH/p6ydy+MHg2HDhnflpptlp6ezoULF4iM\njOTs2bM4ODhQoUIFKlSogJOTkyJFRUxqr0uXYOFC6VG4MPTvDz16QIEChspVBWq+x+Lj49m+fTs7\nd+5k165d5M2bF39/f/z9/fH19aVIEfOnBNdj8uqJyVuFk3+e5LRkdl3YxZqINWyK2sTC9gtp49HG\nZP1lFzW/mcjJ2LFw/75Us8RYtGKzjIwMYmNjnzn81NRUXF1dKVOmDK6urjg6OprF6ZvFXunpsGsX\nzJsnxesHD4aRI2VPaWsu1HaPpaens23bNubMmcPBgwefOXU/Pz9VFK3Rnbx6nLzFLddnhl0OO1qV\na8WC9gvY1H0TAzcN5Jejv7z2+dYUXzLXWNPSpIle797Ze50WrsWbNNrY2FC6dGlatGjB8OHD6dev\nH66urly6dImFCxcyY8YM1q9fT3h4OKmpqUb1pTi2ttCsGaxaBceOwc2b4OFBYECAlOpQRqwpJp+a\nmsqkSZNwd3fnm2++oUOHDsTGxrJy5UoGDBiQbQevpT0SOoaRQ2kBSlKvZD0WdVjEpzs+5f067yst\nx2qYNg3Kl5fi8taKIAg4Ojri6OhIjRo1EEWRxMRELly4wMmTJ9m6dSvVq1endu3aFND4UjeurjBn\nDnz1lbR0P2gQLFmi57A3gMTERMaMGcPhw4epWbOm0nJ0NIDVLde/zIR9E0h4kMDMVjPN1uebUNuy\noNyEhUknrYKDQa607pZos8TERI4dO0ZYWBiurq40bNiQ4sXl2T+iqL2SkqTkCGPGQLduxrdnJtR0\nj5UqVYqdO3fi4eEhe9tyoS/X68v1quD87fNMPzKdQbUGKS3FKrh1Czp1gp9/ls/BWyqFChWiRYsW\njBw5klKlSrFixQqWL19OfLzGT4fkzQutWsGFC0or0Sxt27Zl2bJlSsvQ0QhW6+RFUeS9De/xecPP\n8XTyfO3zrCm+ZMqxiqIUg2/XDrp3N6wNLVwLuTXa2dlRr149hg8fjru7OytXrmTTpk2IoqgJe7xM\nYGAgnDkDFeQrMmVNMXmAgQMH8scff8jerlyoyW46VhyT3xq9lRtJNxhRd4TSUqyC+fMhPh7WrVNa\niTbJkSMHderUoXr16syfP5/Q0FClJRnOmTPSpgwdgyhXrpyeX15jJB7ZoFjfVhmTT01PpebvNRnr\nM5bOlTqbtK/soqbYn1zcvw9lysC+fVLOermxRJu9ievXr7NgwQJGjx5t0OsVtVdysnRm/t49TZWe\nVdM9lp6eTp48ebh37x65c+eWvX05MNReD477mEqS2bCvvvc/MfkS7T/K9HVXNvyox+Tl4oegHyiR\nvwSdKnZSWopVsHy5lMbWFA7eGsmbNy85cmh0ES46WtqQoSEHrzZsbW3x9PTk+PHjSkvR0QBW5+RD\n4kP48fCPzG49O0uJR6wpvmSqsa5cmf0z8a9CC9fCHBqfFhHRgj1eJnDuXJD56Je1xeRB2mEfFxcn\ne7tyYFSblwTtP1SGVTn5BykP6LamG7NazaJMwTJKy7EKkpLgyBFo0kRpJZZBUFAQ0dHRdOqk0VWo\nLVugTx+lVWieM2fOULFiRaVl6GgAq4rJb43eyuQDkwnsE2iS9uVATbE/OdixA775Bg4eNF0flmaz\n1xEWFsaePXt47733yJ8/v8HtKGovZ2c4ehRcXIxvy4yo6R6LiYmhfv36XLlyhZw5c8revhzo5+T1\nmLwihCWEUdNZzxJlTnbsAD8/pVVon8uXL7Njxw7eeecdoxy84nh6gpZPBqiAefPm0a1bN9U6eB11\nYVVO/uT1k3gV9crWa9QUlzM1co9VFOGvv6Sz8XKghWthKo2HDh36T0UxLdjjZQJr14ZJk6SbQ642\nrSgmf/ToURYsWMCoUaNkbVdO1GQ3HWtz8tdOUqVoFaVlWA1nzkBKiuz7rKyOx48fc/78eapUsYB7\n198fUlOltIc62eLcuXMEBAQwa9YsXDQW7tBRDo2ew8k+6RnpRN+Kpnyh7CXh8PX1NY0gFSL3WEND\noV49+eqQaOFamEJjQkICTk5O2NnZmbwvU+PbpAmsWQPe3lCwoCzHLkxlBzXZN2/evHh7e/Ptt9/S\npUsX2do1xRiNafNq0D35hOgAVuTk7zy+g52tHfly5VNaitUQGwulSimtQvs8evQIkFIxm6PevMlx\ndYXt26UjF/b20FldCanURmpqKm3btmXevHm0bdtWaTk6GsNqlusd8ziSnJ7Mw5SH2XqdNcWX5B5r\nyZJw5Yp87WnhWphCY/ny5Xn8+DFRUVEm78vUPNNcsaJ0nG7IENi2TZ42ZUYt9t2+fTtOTk4mcfB6\nTN7ysRonbyPYUL1YdXae36m0FKvh/n2wsZo7zHTY2NjQokULtm7dSkpKitJy5KN6dVi4ED77TGkl\nqmbp0qX46UdUdAzEqs7JLz25lLmhc/Vz8maicWP44AOpvKwpsSSbvYklS5bg7Oxs9Bu+quyVlgaO\njnDuHBQrJn/7MqGUzRITE3F3dyc6OprChQsb1ZY5MdRe0aO0v0u37NQQ/Zy8UnSt3JW4e3H6bN4M\nbN8Oly5JpcN1jCMuLo7FixeTmJhoebuqZ8yQqhcVKKC0EtXx8OFD2rRpw5AhQzTl4HXUhdVsvAPI\nZZuLSX6T+GLXF/i7+WfpNYGBgaraZWtK5BprejqMGAHTp4OcRbK0cC3k1JiYmMj27dtJSEjA29ub\natWqYWtra5K+zMULmjdvhp9+gqAgyJNHnjZlRCn7Pnr0iPXr1/PTTz9RuXJlJk6cqKkxGhOT9znr\nLZ8QxQhRWsALWJWTB+hUsRPDtw4nKjEKj0IeSsuxSP76SzodpW8ENozk5GT27dvH8ePHeeutt+ja\ntat2q869jps3YcAAWLFCP4KBdHLi2LFjLFy4kFWrVlGzZk0+/PBDunbtahknKrKMNY3VPFhVTP4p\nnVd1pnW51rxX/T2T9mMIqoqXGkjjxjBsGMh4nPeNWILNnmfevHkUKlSIpk2bYm9vL3v7qrDXhAlS\n2dmFC+Vr04SY0maJiYn06tWLM2fO0LdvX3r16kUpjX/wMdReWYldq52XY+t6TN7M3H18lz0X9tCy\nbEulpVgkDx7AsWPQUjevQSQmJnL37l06dOhgEgevGlxc4PFjpVUozrFjx6hZsyYVK1bkzJkzfPXV\nV5p38Drqwqqc/N3Hd+m0qhMBlQNwdnDO0mus6cynHGM9cgSqVYN8Jsg5pIVrYazGpKQk0tLSOHDg\nAGlpaSbtSwmeafbxgZ07jT4j/0KbMmNq+96+fZs2bdrw448/8sMPP7yx4IyWxqjF+9KSsRonn/Ag\nAe9F3lQoVIFZrWYpLcdiCQmBWrWUVqFdXFxcGDhwIHFxccyePZuIiAjS09OVliU/pUvD+vXw7ruw\ncqWsBWu0wqRJk2jfvj2dTH3GVMeqsYqY/M2km/gu8qVrpa6M8Rmj6o0sqoiXGkGfPtCwIfTvb74+\ntW6z1xEVFcXBgwe5desW1apVo0aNGjg6OhrdrqrsFRws5a93doZp06BqVfn7kAFT2Kxdu3b07NmT\ngIAAo/WpDT0mr8fkzUb8/XiaL2lOG482qnfwlsCFC/9n77zDorjWP/4ZbKg0RapS7AUsiGjUqNiC\nJdYk9p4bY0wzUfO7Nz3mxvSbmJjo1USjXjXGFkvsBbugKCooKFKkCFIEQZS28/tjhNiFZXdnZnc+\nzzMPy8qe+Z53xn3nnPec94VGjeRWYR40a9asbCFWcXExv/zyC8uXL+fgwYPExMSQn58vt8TK06ED\nnDkj5a8PCpJG9vv2SfswzZwuXbqwZs0aCgoK5JaiYcaYtZMPjg+mw6IODGsxjM97f66Xg7ek+JIh\n+pqZCXeVPDcoargWxtDo5OREUFAQb731FgEBARQUFHDkyBFmzJjBvHnzWLt2LUeOHCEuLo7bCl/M\n9lD7VK0q5bCPjpZG8rNnSwvz3npLWsX5hBkENcWr72bq1KmUlJTQvn17jh8/LosWLSZv/pjZ5luJ\nwpJCPjv4Gf8N+y8rhq2gb+O+ckuyGPLzK5XXROMxVK1alZYtW9KyZUsAPD09ad26NSkpKaSkpLB/\n/35SU1Px8vJizJgx6pu1sreHWbOkIyoKVq+GMWOkp8ZDh+CuREDmQN26ddm4cSNr165l2LBhuLm5\n4e3tjZeXV9lR+rvSQ0saysXsYvKnr55m0qZJeNh5sGjQItxt3Q2ozvgoKl6qBy1bSuXCfXxMd061\n28yQ6HQ6lixZQseOHWnTps1D/0ZV9hJF6NpVSqE4cqTpz38HY9ssLy+PCxcuEB8fT0JCwj1HfHw8\nOp3ukQ8A3t7eODs7K+qhTovJKycmbzYj+dLR+4KTC/jmmW8Y32a8om56S8HGBm5WrJqvhgGxsrKi\ncePGREVFPdLJqwpBgKlTpRX4Mjp5Y2NjY0NAQAABAQEP/ffs7OwHHH9oaCgJCQnExcVRUlJCixYt\nymZ6Sl83bNjwnlTISudLN/UXuxjHf+SWcA9m4eRPXT3F5E2T8bDz4PTLp6lvV99gbasxP7i+GKKv\ntWsbz8mr4VqYUuPd5youLiYnJ4eUlBTCwsKYMkV52RxBD/uIorSa8zGFedSU111fwsPDCQwMpO0j\ndh9kZGQQFRXFhQsXuHDhAvv27SMqKorU1FSaNGlS5vxbtmyJv78/TZo0QRAExeWu1zA8qnby2bez\n+WDfB/xx/g++6vMVE9pO0EbvMmNtrSUyMwalTjw7O7vsOHLkCLGxsWRnZ5Ofn4+dnR329vY899xz\n1K1bV27J+lNYCAcOwKZNsHkz1KgBS5bIrUrR1KtXj6effpqnn376nvfz8/OJjo4uewBYt24d//d/\n/8fNmzfp1KkTLi4uFBUVERAQgIODg0zq/0b7/v4bQRAGAWdFUUy48/uHwHNAAvCmKIpx5WpHaXHJ\n8saxdsTsYPKmyQxpPoTPen2GYy1HE6gzPqqKlz6E/v2lGvKmLDGrdpuVIooiWVlZpKamkpaWxvXr\n18sc+q1bt7Czs8PBwQF7e3scHBzuOWxtbbGyKt9mGcXa6/x5+Pe/YccOaNYMhgyRjpYtpWl7GVGs\nzfQkJSWFkJAQjh8/zvHjxwkLC8PT05OnnnqKPn360L9//0rlZNDXXitf2av3OZXC2AW9DRKTFwTh\nLPCUKIr5giA8C/wHGA34AS+IohhUHj2qHMkXlhTy8taXWTZ0Gc80fkZuORp34e4u1ZHXeDzFxcVc\nu3aN1NTUsiMtLY2aNWvi5uaGs7MzTZs2LXPiNjY25XbiqmTdOmkb3T//Cd98I91IGkbD3d2dYcOG\nMWzYMEC6HyMiIjh69CirV69m2rRpBAQEMGTIEAYPHoy3t7e8gi0TURTF0mQYw4FfRVEMA8IEQZhe\n3kZU+a2xNnItTeo2MYmDt6T4kiH62qmTlL/eGKjhWjxOY2kJ2QULFvDll1+yefNmrly5Qt26denV\nqxczZsxgxowZjBw5kp49e9KmTRs8PT2xs7N7qINXgz3u56Galy6Vts3t3AkzZ1bYwatpD7m+GLuP\nVatWpV27dkyfPp0tW7Zw9epVXn/9dcLDw/H19aVDhw7lzsGgJLupHEEQBBtBEKyA3sDd0xzW5W1E\nlSP5/fH7Gd5Cy/esRLy8pEGZxt8UFRURGhrK0aNHadSoEYMHD8bFxcX8asTry19/wRdfQPv2civR\nuEPt2rVp0aIFK1aswMHBgenTp2NtXW6/omEYvgfCgRvABVEUTwIIguAHXC1vI6qMyfdc1pPXAl7j\nuVbPmUiV6VB77O/kSWnH06lTpjunkm1WWFjIokWLcHZ2JjAwEGdnZ6Of80kozl7du0tT9aNHG6d9\nA6A4mxmZv/76i4kTJ/LOO+/w+uuvU7OCGa60mLxh9skLglAfcAbOiKKou/OeG1BNFMUr5dGjyqHE\nS+1f4ssjXzKs5TCsBFVGHMyWxo0hJkba+aQtlIV9+/bh7u6uVRp7HHPmSJntevUCFxe51Vg8iYmJ\nTJkyhU2bNtG1a1e55VgsgiDcPbXV7iE7D8rl5FXpIUf5jqKqVVW+OvKV0c9lSfElQ/TVwQEKCoyz\njU4N1+JujYmJiURGRtKvXz+jn0stPFRzYCBMmSKVMNTpDNOmAVCSfU3Zx9dee43XXntNbwevJLup\nnJPAMuCbO8e3dx3flLcRVTp5K8GKP174gx9Df+Svi3/JLUfjLq5elVKQW3r++uLiYjZv3ky/fv2o\nVauW3HKUz0cfQXY2zJsntxKL5uDBg5w5c4bZs2fLLUUDZgI5wC1gKTBIFMWed45e5W1ElTH5Ug4l\nHGLU+lFcePUCdjXsjKzMNKg59ieKUmnwqlVNm7tEiTY7f/48oaGhTJw4UXEJPpRoLwBiYyEgAE6f\nBk9P456rgijWZgbm559/5sSJEyxdurRS7WgxecPlrhcEoSHS/vghSIlw5oqiGF5ePaocyZfSzasb\nQY2DmHtortxSNICFC6XS4PPny61EfiIjI2nTpo3iHLyiadQIXnsN3nlHbiUWS6dOnThy5AglJSVy\nS9G4w53MdpuAXUBHoFlFPq9qJw8wrcM0dl7eabT2LSm+VJm+RkfDBx9I2+eMNTuthmtRqjEnJ8fo\nK+nVYI/7eaLm//s/OHZMSmtrqDb1REn2NVUf/fz88PDw4IMPPjBYmxr6IQhCI0EQ3hUEIQT4BDgD\ntBBF8Y+KtKPK1fV306JeC6IzouWWYdFkZ8OoUfDJJ9C0qdxqlIEgCNy6dUtuGeqjVi0p492MGdI+\nTG0mxKRYWVnx+++/06FDBzp06KDtCpGXGOAs0ij+BuAJTC+dHRRFsVzl7lQdkwdIzUulzYI2XJt9\nzYiqTIfaYn9ZWfDMM9CtG/znP/J8JyvRZhERERw8eJCpU6cqLumNEu11D6IIbdvCV1+BkXYmVBTF\n28zAnDx5kgEDBnDw4EFatGhR4c9rMXmD5K7/GHjkDSSK4ifl0aOsbx89SMhOoIFdA7llWCwvvQRd\nu8rn4JWKj48PERERHDp0iJ49e8otR10IgpTm9vvvFePkLY0OHTrwr3/9i/fff591WgpLWRBF8WND\ntKP6mHzY1TDauxkvHaYlxZcq2teoKDh0CD7/3DQOXg3XolSjIAgMHDiQkydPkpqaatRzqYlya37+\neakIwtUnZ+/UYvLGadfHx4cbN24YtE0N06N6Jx+dEY2Pk4/cMiySb7+FV1813kI7tWNra0vfvn35\n888/KS4ulluOuqhVC4YNg1Wr5FZiseTn51OjRg25ZWhUEtU7+bzCPKPukQ8MDDRa20qjIn1NTYX1\n66UdT6ZCDdfifo1t27bFwcGBgwcPGv1caqBCmidMgBUrDNtmBVCSfeXoY0hICD4+FR9AKcluGmYQ\nk79ZdJPa1WvLLcPi2LMHevcGR0e5lSgbQRDo06cPK1eupFevciep0gBo105KkKNhUoqLi3n//fdZ\ntWoV27dvN+m5O9poORIMjepH8rmFudhUtzFa+5YUX6pIX0+dAn9/42l5GGq4Fg/T6OjoyM2bNw2+\npU4N9rifCmlOSwNbW8O2WQGUZF9T9fHkyZMEBgZy+vRpTp06pddIXkl20zADJx+fHY+HnYfcMiyO\nixehZUu5VaiD9PR0atasqdXjrgg6Hbz+ulS3WMPoXLp0iREjRjB48GDGjRvHtm3bqFevnsl1CIL6\nD6Wh6n3yxbpibD+3JfOdTGpVM4/VX2rZj9umDSxbBn5+Jj3tQ1GyzUpKSli7dn1PjKEAACAASURB\nVC1OTk707t3b6OcrD0q2FyCVMHzzTSlH8uHDUjEEmVG8zfTk4sWLfPHFF2zZsoW3336bN954g9q1\nKx/+1NdeMe+YeHrQCDT5Ksyguesri6pH8vHZ8bjUdjEbB68mBAGsVH33GJ+ioiJ+//13BEGgR48e\ncstRB9HR0KmTlEZx505FOHhz5Ny5c4wePZquXbvi7e3NxYsX+de//mUQB6+hLFT9NZ2Yk4iXg5dR\nz2FJ8aWK9PX2bdN//6rhWpRqFEWRDRs2YG1tzfPPP2+UrHdqsMf9PFLzrVtSXuSuXWH6dPj9d6lm\ncWXarCRKsq+htISFhTFs2DD69u2Ln58fy5Yt48MPP6ROnToGaR+UZTcNlTt5QRAQUGAQxMzJzYXE\nRGhWoVpIlkVwcDD5+fkMHTqUKlWqyC1H2ezYAb6+cO6ctKLz5ZeVGdxUMaGhoQwYMIAhQ4bQs2dP\nYmNjeeedd6ilJbkwe1Qdk4+4FsFzfzxH9GvmU6BGDbG/t96ClBRYs8Zkp3wsSrTZd999x/jx42VZ\nvPQkFGOv69elm+ngQViwAIKCDNu+AVGMzSrItWvX+Oc//8mOHTv46KOPmDRpkkkS3GgxeeXE5FUd\n8GpRrwXJN5LJuZ2DvXX5pvY09KekRPou3rhRGnBpPBqdTkdsbCzW1tbY2Bhvi6dqOXcO+veHIUPg\n7FnQbGRQkpKS+O2335g3bx4TJkwgKioKOzvjJQ0zFGcnqr9ADV85yK3gHlQ9XV/VqirtXNsRdjXM\naOewpPjS4/oaHAwdOsDq1fDXX1C3rslk3aUh2PQnrSClGvv160d8fDzz589n0aJF7N27l4SEBEpK\nSgx+LjVRpvnXX2HKFPjpp0o7eC0mL3H79m3++OMP+vfvT5s2bUhMTOTAgQN8++23j3Twxuijkuym\nofKRPECAewAnkk/Qq6GWTcwYFBVJIdL9++HLL+GFF7RwaXnw8fHBx8eHkpISkpKSuHz5Mjt37iQr\nKwtvb28aNmxIw4YNcXJyQrBEgx49CgMGSPvhtW0aeqPT6Thy5AirVq1i7dq1tGvXjsmTJ7N+/Xp1\nxtst8f+CkVF1TB5g1blVbIzayNoX1hpRlelQUuwvP19y6lZWUvxdqd8ZSrLZk8jLyyM+Pp7Y2Fji\n4+MpLCwsc/gNGzbEwcHB6E5fEfY6fVpaRS+K0mje1OkTK4gibHYXZ8+eZdWqVaxevRo7OzvGjh3L\n6NGj8fIy7m6j8qKvvTaczzGWJJMxvJW9FpM3JD5OPsw9NFduGWZHdjYMGgTe3rBkCVSrJrci88DG\nxgZfX198fX0ByM7OJi4ujri4OPbv3w9AgwYNqF+/Pg0aNMDd3Z3q1avLKdk4+PnBkSNSRqWBA6Un\nyIAA6ejQQXL65Uhpa0kkJCSwatUqVq1aRU5ODmPGjGHr1q20bt1abmkG49aPJ+WWYHaofp4s+3Y2\ndWoabo/n/VhSfKm0r2lpEBgofQ8vW6YcB6+Ga1FRjQ4ODvj5+TF8+HDefvttpkyZgo+PD3l5eezd\nu5dvvvmGhQsXsmXLFk6fPk1ubq7e51IC92i2soLJk6WtGtu2wbPPSnsz330XXF2hVSupEt2PP0Jo\nqBQ7elKbxtIqEzdu3GDBggW0bt0af39/rly5ws8//0x8fDxffPFFpR28FpM3f1Q/ks8tzDVqqVlL\n5B//gL594auvtBCZKREEgTp16lCnTp2ykX5xcTFpaWkkJSURGxvLrl27aNWqFU8//bTMag2IlRW0\naCEd48dL7xUVQWQknDwJJ07A4sUQHw9dukCPHtLRoQOY4ywHUFBQwM8//8wXX3xB9+7dGT16NLNm\nzTLPWR0No6L6mPyhhEP8a++/ODzlsBFVmQ65Y3/btklbl8+eBRNspzUIctvMlNy8eZOQkBBOnjxJ\n06ZNGThwYIW/+FVrr8xMOHQIDhyQtnvExMBTT0G/fjBjBhgx6ZApbbZjxw6mTZtGmzZtmDt3btkD\nn5rQ114rX1H/FrqxC3prMXlD0tSxKZHpkRTriqlqpfruyM6aNfD22+px8JZG7dq16dWrF/Xq1WPP\nnj3I7nhNiaMjDB0qHSAl0zl8GL79VlrIt2yZUR29qTh8+DDdu3dn+fLlckvRMANUH5N3tXHF096T\nPbF7jNK+JcWXgoODOX1auQud1XAtTKHx2rVr7Ny5Ew8PD5NkLzMkBrVPnTowaBDB//oXXLsG77xj\nuLaR73578cUX2bZtG0ePHjW6Fi0mb/6o3skDfNnnSyZvmkxMVozcUlRP7dpSrRANZXL16lWWL19O\n//79cXJykluOMqhRA0aMgPR0uZUYhIYNG/Lzzz8zYsQIxo4dS1JSktySNFSM6mPypSwKW8RHwR/x\nD79/MLHdRJrUbWIEdcZH7njpyy/DjRtSQjKl7ou/H7ltZiqKi4v57rvvGDhwIK1atdK7HbOwV0kJ\nnD8Px47B8eOwaxd8+CFMnWqU08lhs7y8PL788kvmz59PixYt8PX1LUuy5Ovri6urq2ITKWkxeS0m\nb3Cm+k/lqQZP8Vv4b3Rd0pVmjs2Y1HYSz7V6DgdrZeUSVjJffy05+o4dpWqfKlzzY7akpaVha2tb\nKQevWjIyICTkb6d+4oS0ze6pp6BzZ3jjDWjTRm6VBsXGxoZPP/2UmTNncvbsWSIjI4mIiODPP/8k\nMjKSkpKSe5y+j48PLVu2xMXFRbHOX8P0mM1I/m6KSorYHrOdpeFL2RO7h6Z1m9LdqzvdvbrTzbMb\nTrXLP80ZHBxMYGBgpfRUBDlHWaV9FUVYuhRmz5Zyk0yZItURkTv8+6hroaSRqbHul4yMDDZs2ECj\nRo3o06dPpc6lhHvssVy/DmFh0va50iM7W7oZS516p07SQrzytllJrXLfYw/r47Vr14iIiCAyMrLs\nuHDhArdv36Zx48ZlR5MmTcpee3h4ULVq1ce2awitPXv21MteeRt7GlSLHNgM26+N5I1NtSrVGNx8\nMIObD6awpJCwlDAOJhzk19O/MmXTFNxt3enu1Z2nPZ+mtXNrmtdrjnVVa7llKwZBkBz76NFSxbn/\n/hdefRXGjIFJk6BdO23/vCkQRZGsrCyioqI4evQoPXv2xF+pqyL1oaQEEhLg0iWpKl2pQ792TcrE\n1KEDPPccfP45NG6s5bi/D2dnZ3r16kWvXvfW7cjJyeHy5ctcvnyZmJgYTpw4we+//05MTAzp6el4\nenqWOX2AmjVr0rFjR230b6aY5Uj+cZToSjibdpaDCQc5mnSUiGsRxF6PxdPek1ZOrfBx8pEOZx+a\nOTYzufOXe8TwKOLipNH9ihVSrH7sWMnpe3sb9bTlQqk2qyi5ubkkJyeTnJxMSkoKKSkp1KhRAw8P\nDwIDA3G8M3KtLCa1l04nZbS7eFFy5pcu/f06Lg5cXKBpUym7XWlK22bNFLcVzlzusdu3bxMXF0dM\nTEzZQ8COHTuoXbs2U6dOZdy4cdjbV75st1ZPXjkjeYtz8g+jsKSQS5mXiEyP5Hz6eSLTI4m8Fkns\n9Vi8HLzKHH8rp1b4OPvQ3LE5NaoaZ+5a6V8moigVEPvf/2DtWum7edgw6NlTConKMdhSus0eRWFh\nIefOnSMmJobk5GSKi4upX78+7u7u1K9fn/r161O7dm2Dn9eo9srIkParHzsmOfKYGLCzkxx5s2b3\n/mzcGGrW1LcbJkWt91h50Ol0bN++nZkzZ5KYmMjLL7/Mt99+W6mRvebklePkzXK6vqJUr1IdH2dp\n9H43hSWFrNy8ktrNahN5LZL1F9bz6cFPicuOw8ve6++Rv7P0ENDMsZnRnL8pKE98ThCga1fpmDcP\nduyA7dulKf2MDCnbaK9e0tGihWGn9U29PkIfyqMxMzOTEydOcPbsWTw9PfH19eWZZ56pcAU6Rdkj\nNFSqJrd5s7SA4/nn/3bmdxWaMVYM2NgxebkxpJasrCzCw8MJDw9n+/btpKamcvnyZZo2bcrzzz9P\nu3btKuXgtX3yykJz8o+hepXqNKzTkECfQLjL/xeWFHIx8yKR16SR/7rz6/gk/RPirsfh7eBNoHcg\ng5sPplfDXmYd669eHQYPlg6A5GSp7vy+fdIq/YICGD4cpk0DMyqUVS5KSkrIyckhOzu77MjJySEz\nM5Pr16/Tvn17pk6dioODGez8GDUK/vgDPvpIGrkbKKygoR+iKJKZmVlW3fDu48KFC2RnZ9O2bVv8\n/Pzw8/Nj1KhR+Pj4qC6xkkb50KbrDUhBcQFRGVHsjt3N5ujNnEk7Q6+GvRjcbDADmw3EubbzE9sw\np2nBy5elGP7ixVLsfto0aYBn6BlaOWxWUlLCjRs3uH79+j1OvPT1zZs3sbW1xcHBAQcHB+zt7cte\nN2jQ4J4VzqbG4PYKC4PPPpNSzE6fDq+9BvXqGUKqYlDa/8u8vDzi4+OJjY19qDOvWrUqDRs2fOBo\n1qwZjRo1wsrIcTV97ZW3MdBIikyHzbBgRU3Xa07eiFzMvMj0v6azN24vdWvWJeXtlCdO5yvty8QQ\nFBfD1q2wcCGEh8OsWfDKK1J2PUNgKpvl5uYSHR1NdHQ0CQkJ1K5d+6FO3MHBATs7O6N/keqL0ewV\nHS3lkV+1CtzdoWVL6WjVSvrZooVqa8TL+f/y1q1bhIeHExoaSmhoKCEhIaSkpODt7f1QR96wYUPZ\nZ4g0J685+UeiNIdVkVhYia6E6MxodsbsZFP0Jk6nnqZPoz4MaT6EgU0H4ljrydOYit/DXEnOnYNP\nPoEjR6R9+NOng3U5Ixpy7JPPzs7m3LlzREdHk5mZSdOmTWnevDlNmjR56PSmKeO4it0nX1goLbq7\ncOHeIzqa4Nq1CfTz+/sBoE0bae97JR6IzG2ffGJiInv37iUkJITQ0FCioqJo0KABvXv3pmPHjnTs\n2JHmzZtTxQA7EJS2T/7mn+rfJ1976IP75N8oR1KxHyLQFt4piRJdCRczLxJ2NYywlDBOXj1JeGo4\nzrWd6endk1ldZtG7YW9qVlPH6mFT0bo1rFsHZ87A++/DkiXSlL6fn9zKHk5ERARhYWHk5ORQp04d\nCgoKSEpK4tatW9StWxdHR0fs7Oy0PcZ3U706+PhIx92UlEhlDu3tJad/4gTMny/Vjn/zTZgwAWxs\n5NGsAPLz8/n8889ZsGABzzzzDJ06dWLChAm0a9eOkJAQxSwC1FAX2ki+HDzKobvUdsHf3R9/N+lo\n79aeOjXrVOpc5jhd/yhEEVaulErbzpqlfxExU9isqKiI69evk5mZSVZW1j0/b9++TZ06dXB0dKRu\n3brUqVMHOzs77O3tsbOzw9raWlEPAYq6x0RRqhE/b55UJ/6ll+Df/7a4ffLBwcFMnDiRLl268PXX\nX9OgQQO9dCoFfe2ljeS1kbzRKY9D/7jZxwZx6JaOIMC4cdK2u969oUEDKcGOEqlWrRrOzs44Oz+4\neLKwsPAex5+SkkJUVBQ3btzgxo0b6HQ67Ozsyhy/ra1t2QNA6Xs1atRQ1IOAyRAE6N5dOtatk0b0\nc+Yozskbm6tXr5KXl8ebb76pegevoSws3skXlhRyLPEYOy/v5NCVQw849KFZQ9k8Y7NFOHS59gV7\neEiJdXr3hj594CF+FFDWvuW7qV69Oq6urri6uhIcHMzg0j2FdygoKChz+Dk5Ody4cYPk5GQuXLhQ\n9p4oimWOv27duri5ueHm5oaTk9MjV+Ir1R6P45GaT56U0td+/DFUq2aYNiuJKe07evRo7OzsGDRo\nEMOHD78n33zjxo05efKkavpYmX3yn62OK3vdzdeB7q2V/7178Nx1DkVkyy3jkVikk7+cdZmdl3ey\n8/JOguODaebYjKDGQXzU4yP83fzvcejBRcEW4eDlxtlZCtmqJAFahahRowZOTk6Prf9eUFBQ9gCQ\nkZHBlStXCAkJISsri3r16uHq6lrm+F1cXKhevboJe2AkSkqkbRf/+Q/Ex8OMGVLRBAtl4MCBHD58\nmD179nD58mUOHz7M5cuXiY2NpUaNGrRs2fIB59+4cWOcnJzMZhbovdEN5ZZQYbq3rnPPw8jcNfHy\niXkIFheTH75mOEcTjxLUJIigxkH0bdS3QlXpjI2i4qUm5IMPIC0NFi2q+GfN2WZFRUVcu3aNq1ev\ncvXqVVJTU0lPT6d9+/b069dPrzYVYa/YWBg6VNpaMXOmVIhGxtwBT0JOm4miyNWrV8uKztx9xMTE\nUFRUxOjRo/n666+xs7Or9PkMgRaT12LysqATdey8vJOUt1Owt658EQYNwxAXBwsWwKlTcitRHtWq\nVSvLY1/KyZMnuXr1qoyqKklIiFTw4L33pD2UZjIKNRaCIODu7o67uzvdunV74N/T09N57733aNOm\nDb/88ktZKWINDQBlZuswEpn5mVhXta6Qg7ekPMxy9XXuXHj9dfD0fPzfqeFaGFPj9evX2b17N/v3\n78fDw0MV9rif4MWL4dlnpSmbV181iIM3lh2UZN/HaXFycmLRokXMnDmT4cOHU1xcbJB29UVJdtOw\nsJE8gJVgUc81iufWLVi/HiIi5FaiXGJjYzl27BjJycm0a9eOKVOm4OjoqL4v05QUePddadrm2Wfl\nVmNW5Ofns3DhQubNmydryuTKInrp5JZgdlhUTD4qI4qBqwZy+Y3LRmnfECgiXmpC1qyBX3+FXbv0\nb8OcbRYZGcnOnTvp1asXPj4+VKvgyvOHIZu9PvwQsrKkBDgqQ+n32Mcff8z58+dZs2aNIhbh6Z3W\n9nQPY0kyGTZ+B7SYvFycSD5BB/cOcsvQuIv//U/aK6/xIJcuXWL79u2MHz8eFxcXueVUnv37pRWW\nGgYlLS2NH3/8kbCwMEU4+EqhcvlKxKLmrk+knCDAPaBCn1HdlGglkKOvoaHS3vjyoIZrYSiNERER\nbNq0iZEjRz7SwavBHmUkJ0NkJMFGGNlaekz+8OHDdOnSBW9vb4O2qy9KspuGpY3kU07wXMvn5Jah\ncYf8fMjJATc3uZUoh4KCAg4fPsyZM2fMZwQPUpGC558HrWa5wTl16hQ+99cJ0NC4g8XE5ItKinD4\n0oHUmanY1lBuuUulx/4MyfXr0KiR9LMymIPNdDodp0+fJjg4mEaNGtG7d2+j7Xk2ub2uX4dmzaR6\n882b69eGzCj1HsvKyqJ58+YcOnSIFi1aGPVcFUHvmHy4GcTk22kxeVlIyEnAsaajoh28pXHrVoUz\nmJoVOp2OK1euEBUVRVRUFPb29owePRp3d3e5pRmWbdvA1VV6otMwCIWFhfzxxx98/fXXjBw5UlEO\nXkNZWExM3svei+u3r5N9u2I5hi0pvmTqvp46Be3alf/v1XAtnqRRFEUuXbrE5s2b+fbbb9m5cyc1\na9ZkzJgxTJo0qUIOXg32AOCFF6QkCBMnElyZbRSPwJJi8pmZmXzxxRc0atSIpUuXMnfuXH744YdK\nt2tIlGQ3DQsayVerUo1A70BWnFnB651el1uOBnDsGDz1lNwqTMvt27fZuHEjVlZWjB492jIqjlWv\nLlWYGz8ehg+H/v2ljHcDB0q15TUeiyiKnD9/nqVLl7Jp0yaGDBnC1q1baVeRJ2QNi8ViYvIAkdci\nCVwWyLlXzuFq42qUc1QWpcb+jEGvXjB7tvSdXxnUZrNbt25x8OBBzpw5Q5cuXejSpQtWVqabVJPV\nXunpsGULbNwo1Y/v0kVy/MOHQ716lW/fSMhhs4KCAlasWMFPP/3EjRs3mDZtGpMnT6aegu1UihaT\nV05M3qKcPMC7e9/l3LVzbB61WZF7StXmsPSloED6Tr9yBepUssifWm2WmZnJtm3bKCws5LnnnsPB\nwcEk51WMvXJzYccOKeXhjh3StM7IkVLhmsreFAbGlDbLz89n8eLFfP3117Ru3ZoZM2bQt29fkz4I\nVhbNySvHyavnrjEQHwd+TEpuCktOLynX31tSfMmUfT16FFq2rNh3uRquRUU0Ojo6Mm7cOFq2bMni\nxYuJiYkx2rmUwj2abW2leP3vv0v76CdPlkb53t7SdH52+dbPmFNMfteuXTRq1IiDBw+yadMmtm/f\nTlBQEAcPHjTK+bSYvPljcU6+epXqfBf0Hf85/h/kHslZMps2aenLQRrxdOnShZEjR7Jx40ZSU1Pl\nliQPtWtLo/gNGyAxUZrm+cc/wML+j8bFxdG7d2/Wr1+Pv7+/3HI0zACLm64HaSFL0x+bsm7EOtq5\nKmvximKmUo2IKIKXlzRD26pV5dszB5uJosjmzZtJTk5m+vTpRj2XKux1+DAEBko3iQJKp5rKZmfP\nnqVPnz4sWLCAYcOGqWqK/m606Xptul5WBEGgvVt7ojOi5ZZikURHg5WVNF2vAampqSxdupRr164x\nZMgQueXIS0SENFU/apRUyKaH+r/0K0KbNm1Yvnw5c+fOpUOHDmzbtk2bcdSoFBazhe5+XG1cuZp3\n9Yl/FxwcTGBgoPEFKQBT9TU0VFpUXdF1j2q4FhXVqNPpWLJkCUFBQbRv375Ci0HVYI/7eazmggJp\n8d2cObBqFdSsWfk2K4Fc9u3Xrx9BQUFs3LiRF198kbVr11JcXKyaPlYmJp+6Ks9wQswIQRC6AN7c\n5bNFUVxens9a5EgeQCfqqGplsc84slJcDNbWcqtQDsXFxfj7+ytyt4dJKSgAnQ7efrvcDt5cEQSB\n4cOH065dO3Jzc+WWoyEjgiCsAL4BngYC7hzlLqdqsV6usKSQ6lWqP/Hv1DZSqgym6uupU6BPDhg1\nXIuKarx+/Tq1a9c2ybmUwEM15+fDr7/CN9/olTTBWHZQgn3d3Nw4f/48M2fONEr7xuhjZdp07ayl\nHX8IHYBW+i6KsdiRfGFJIdWsLDhxukykpUkzsUZeW6YaIiMjaWWI1YdqpKgIvv5aymm/bx/88Ye0\nZ16jjEmTJvHrr7/KLUNDXiIAvbO3WexIPi47jrGtxz7x79QY99QXU/T1++9h9GipXklFUcO1qKjG\nrKwsvLy8THIuJVCm+cwZaV+8iwvs3QuVKJVqbjH5UnQ6HQsXLqRVq1aq6mOlYvLHtNBEKYIgbAFE\nwBY4LwhCKFBQ+u+iKA4uTzsW6eRLdCWcvnqaDu7lDmtoGIC8PFi0CMLC5FaiHARB4ObNm3LLMC07\ndkhpbOfPlxy9pa9FuA9RFAkJCeHLL78kIyODXbt2ERISIrcsDdPzjSEasch98pHXIhm6ZiiXXr9k\n1PPogyr2MOvJmjXw22+wfbth21WzzdLS0lixYgWDBg2iuYlqrctur7Q0GDRIqjH/yy+qWIVpCpvl\n5uaycuVKFi5cSF5eHi+//DLTpk3D1lZ9cWp97bXylT3GkmQyxi7oY5R98oIgOALdgSuiKJZ7qGSR\nMfkTKScIcA+QW4bFsXWrtAVa429cXFzo3r0769evp6SkRG45psHFBYKDobBQisePHg0//QTh4WAp\nNriP9evX07BhQ3bv3s3XX3/NxYsXmT17tiodvIZhEARhqyAIvndeuyHF5qcAKwRBmFHedizTySeX\n38lbUh5mY/c1NhZatND/82q4FhXRWFJSQmhoKAcPHmTEiBFUqVLFaOdSCmWaa9WSpnaCg+GZZ+D0\naSkBTt26EBQEn34qLcbLyHhials1564vKirirbfeYtasWezYsYP169c/tBiNmvpYuTYFMzgMRkNR\nFCPuvJ4M7BZFcRDQCcnZlwuLi8mfTTvLugvr2D1+t9xSLI6CAinTnaVSVFREcnIyCQkJJCQkkJyc\njIuLC1OmTKFu3bpyyzM9giBN2TdrJsXmQSpFe/QoHDkCH3wAkZF//13Tpn8fpb+rvB59dHQ0Cxcu\nZMyYMXovwNQwW4ruet0bWAwgimKuIAi68jZiMTF5URQ5dOUQo9aN4rug7xjpO9Lg5zAEssdLjcg7\n70jh1zlzDNuu0mwmiiJ5eXnk5OSQk5NDamoqCQkJpKam4uLigqenJ15eXnh6emItQzxaafZ6LKIo\njeYvXZKOixf/fn3pkjQjcLfTHzECmjQxuAxj2iwlJYW5c+eyevVqpk6dir+/P46OjvccctwnlUH/\nmPxeY0kyGWMX9DZITP7O6vpdQBKwBGlkny0IQk3gpCiK5dqSYvZOPiU3hRVnVrA0fCk6Uce/e/2b\nET4jDNa+oVHVF3AFOXcOevWC48ehcWPDtWtqmxUWFnLjxg1ycnLIzs4mJyen7PfS19bW1tjb22Nv\nb4+TkxNeXl40aNCA6tWfnIDJ2JjNPSaKcPXq3w5/2TKpmM1HHxn8VKawWUJCAj/88APx8fFkZmaW\nHRkZGVSvXv0ep1+vXr3H/u7o6Iitra1sWRQ1J28QJ+8MzAHcgJ9EUdx15/2egL8oiuVafW920/X5\nRfmcvnqakOQQ9sTu4XjScZ5r+Ry/Dv6VLh5dKnzTy71X1pQYu6+tW0szsCNHwokT6shdX1xczIkT\nJ0hISChz4kVFRWUO3M7ODnt7e7y8vLC3tyciIoIBAwZQtarx/2up8d40qGZBAHd3gi9eJLBbN1i9\nWorpGwhT29fLy4tvv/32gfdFUWT79u20atWqzOnf/RBw8eJFjh079sCDQWFh4UMfBJycnHBycqJe\nvXqkpKTQp0+fsvcMMWOgxrUiSkQUxWvAtIe8vx/YX952VO3kS3QlXMi4QEhSCKHJoYSmhHIx8yI+\nTj50rN+RCW0nsPaFtdSurl/aUA3D8/rr8MMPUi6Udsqq8nsPoigSHR3Nrl27cHJyom3btjg4OGBv\nb0+tWrUe+bB45coVkzh4DSA5WVq899//Qm6utHBvzBi5VRkcQRCoVasW3t7eeHt7l/tzBQUF9zj9\n0p8ZGRnExcURGhpKdHQ0q1atIj09vWzGoPQBoNTx33/c/W9yzhaYO4IgfC+K4oy7kuLcQ3mT4ahu\nuj7rVhabojax9vxaDl85jJutGx3rd6Sje0c61u9IO9d21Khaw4SKDYvZTKU+htdek+rJz55tmPaM\nYbMDBw4QHBxMkyZN6NGjB25ubhVe/a5UVHmP3b4tlaE9fVo6Tp2S4vPDGMKH3AAAIABJREFUhkmO\nPTAQjHh9VGmzCiKKIrm5uaSnp99zZGRkPPBe6VFcXEy9evXw8fHhjTfeoH///lhZWWnT9YaZrvcX\nRTFMEISH1lsWRfFAefSoYsiRdSuLP6P+ZO35tRxNPEqfRn2Y0HYC/xv+P+rWtMBVySqnTRtQegKv\njh074ujoyJUrV/jrr7/IzMzE3d0dDw8PPD098fDwUN1iKNWQnS3tmS916OHhEBMjLazz85OOUaOg\nY0dVJNJRC4IgYGdnh52dHY3LuWjm1q1bZGRkcODAAd5//31mz57N22+/bWSllkFpwptSZy4IQjXA\nF0i+M5VfLhTt5EVRZGn4Ut7Z/Q49vHswse1E1r6wFpvqNibToMa4p76Yqq+enrBuXcU/Z8prUbNm\nTXx9ffH1lR7Bb9++TVJSEomJiRw9epSUlBTq1q1bNoXq6elJzZo1TapRjffmPZpFEVJS/nbmpQ49\nPV16EvTzgx49YMYMKbd9jYfP0Kkpr7u+KLWPNWvWxMPDg3HjxjF27Fj279+vOXkDIQjCQuBHURQj\nBUGwB44BJUBdQRBmiaK4ujztKNbJZ9/O5qUtLxGdEc2BSQfwcda/gIWGsqhZU5p9VRPW1tY0adKE\nJne2Z5WUlJCSklIW29ywYQOOjo5kZ2fj6uqKq6sr9vb2WrzybjIypAQ327f/7dDh79H5yJHw+efS\nFjgzCY1YEoIg0KtXLyZOnKg5esPQTRTF0oV3k4GLoigOFQTBFdgOlMvJKzImn3M7h74r+tLWpS0/\n9P8B66qWMyVnCbG//fvhk0+kZGeGQAk2KykpITk5mfj4eK5cuUJaWhqFhYX3LFhydnbGyckJOzs7\nWZ2/Se2l00kXevFiybkHBoK//9+O3d1dFQVqlHCPqYG4uDjGjBnD8ePHtZj8HSoRkz8tiqLfndd/\nAWtFUfzt/n97EoocyQ9cNRB/N39+GvCTNhIyQzIyoF49uVUYlipVquDp6Ymnp2fZe7du3SpboHTt\n2jViYmJIT0+/x/k3bdrUfOvJr1wJH38sxc2nToWff4Y6deRWpWFgCgoKOHPmDAcPHuSrr77in//8\nJ8ePH5dbljmQLQjCs0Ay0BV4EUAQhKpAzfI2okgnn1eYx/wB8xXh4JUUlzM2puprerp+Tl4N1+Ju\njTVr1nzA8YPk/K9du0Z6ejp79+4lPj6eoKAgvXLXK9oe330nJaYZO7ZstG6s+uVKjFcbEqX0URRF\nYmJiCAkJITQ0lJCQECIiImjatCkdO3Zkx44d3Lhxw+A6LZSXgR8AV2CGKIqpd97vDfxV3kYU6eSD\nGgdhJVhwknMzJz0dnJzkViEfNWvWxMvLCy8vL3x9fdmwYQOrVq1i7NixDxQmUTUuLtIoXgEP6xr6\nc/PmTdavX8+aNWs4duwYtra2dOrUiY4dO/LCCy/Qvn17atf+OxeJlgzHMIiieBHo95D3dwI7y9uO\nImPy/z7wb97r/p7cUmTBEmJ/r78u7YZ64w3DtKd2m4miyNKlS2nXrh3t27c3+vlMZq81a6SFdCdP\ngsoTBKn9HqsoOp2OQ4cOsWzZMjZu3EjXrl0ZP348PXr0wNXV9Ymf1/bJG76evL4octiQeCNRbgka\nRiQ5WRrkaUgIgkD37t3NL445YgQ4Ohq+IpGG0RkyZAivvvoqPj4+XLhwga1btzJy5MhyOXgNZaFI\nJ7/+wnpuFytjj5UlTT2Zoq86HRw6BJ07V/yzargW+mqMioqiWbNmJjmXyRAEafHdypXS6nqUWL/c\n9O3qgyn7mJCQwLFjxzh16hQzZ86ssGNXkt00FOrk/d38WRa+TG4ZGkYgKkoqAX7fWjSLJicnh8jI\nSLp06SK3FMPj6go7d8KsWXD9utxqNMpBcHAwffr0UUTFRI3Ko0gn/2nPT5lzcA43C2/KLUUxq2tN\ngSn6evIkBATo91k1XIuKahRFkeDgYPz9/alVq5ZRzyUbTZpA9+6wc6dRNBvLDkqyryn7WKVKlUoV\nWVKS3TQU6uQD6gfQ1qUtG6M2yi1Fw8BcvgwVnJU2S0q3Ii1atIj09HTzHMWXotNBWhqY084BM8bF\nxYXERG1dlLmg2CWvo3xHse78Osa1GSerDiXtlTU2pujrjRv6T9Wr4VqUR2NycjJ79uwhNzeX3r17\n06JFC71yQqjBHgAsWgTVqsHzz2v75PXElH0MCAggLCyMkpISvSovViYm/2UDLZ2xoVGsk+/XpB9v\n7ngTnajT9sybETdugJ2d3CrkISMjg3379pGUlESPHj3w8/Mzr33xDyMiAj74AA4c0EbyKkMJycg0\nKo8i98mXamryQxM2jdpkUcVpzH0/7siRMHQojB5tuDaVbrO0tDSOHTvGpUuX6Ny5M506daJatWom\nOffDMJm9zp2D4cPh/fdh4sSKfVZhKP0eMyRHjhzh7bffJqQS9aD1tVebueUqka5ozr7bQ1H75BU7\nkgfo7NGZY0nHLMrJmztubtI+eXNHFEViY2M5duwYaWlpBAQE8Nprr1GzZrlTTqsXnQ7mzYO5c+Hr\nr1Xv4C2NiIiIshLLGupH0U6+h1cP/oj8gxf9XpRt6khJcTljY4q+tmoFR47o91k1XItSjXv27OHi\nxYt06dKFUaNGVWq18pPOpShOnZJSGgKEhECjRvf8sxaT1w9T9bGgoICVK1cyZsyYSrWpL5khm/T+\nrMbDUXSQbGLbiaTdTGP5meVyS9EwED16SKVmVTiLWW6SkpI4e/YskydPxs/PzygOXnGkp8PLL8OA\nATB5spTx6D4Hr6FsiouLGTduHM7Ozrz00ktyy9EwEIqOyQOcvnqafiv7MaXdFD4K/Mjsa8ube+xP\nFMHBAeLioG5dw7SpNJtt27YNW1tbunXrZpT2K4tB7ZWZCd9+C//9L4wbJ5WWNcNyskq7xwxNamoq\nY8aMwdramo0bN1KjRo1KtaevvfI29qzUeZWAzbD9iorJK3okD+Dn5seZaWe4mHURv//6cSjhkNyS\nNCqBIEiFyQoK5FZiPKpXr87NmzdRyxe83nz2mZT0IDNTmqafN88sHbw5k5GRwYoVK+jQoQPdunVj\ny5YtlXbwGspC8U4ewNXGlXUvrGNO4BzGbhjLyHUjic+ON8m5LSkPsyn6eumSNJrXZxSvhmsRHByM\nj48PsbGxLF68mPPnzxvN2ctqj2PH4Jdf4MQJaRTv5VWuj2m56/XDUFqKi4s5fPgwH3zwAR07dsTL\ny4u1a9eydOlSPvnkE732xd9PZbQKgvoPpaGaYKEgCLzg8wIDmw3km6Pf4L/In4ltJ/JGpzfwdvCW\nW55GOZk/H/7xDzDnwYKbmxuvvPIK0dHRHDp0iP3799O9e3d8fX3NZ+/x/Pnwwgta3F3h3Lp1i7Cw\nMI4fP86RI0cIDg7G29uboKAgvvrqK4qKiujbt6/cMjWMiOJj8o8i+UYy3x3/jt/Cf6OHdw/e7PQm\n3Ty7qf5L1JxjfzdugLc3nDkDHh6Ga1fJNivdSrd//36Kioro2bMnzZs3l/U+NYi99u2D8eNh7Fj4\n9FPzfmpD2fdYKaIoEhcXx/Hjxzl+/DjHjh3j/PnztGrViqeeeorOnTvTq1cvk5SL1ddeN/9Uf0y+\n9lBlxeRV6+RLySvMY/mZ5fwQ8gMe9h5sGLEB2xq2RlRoXNTwZaIv8+bB4cOwdq1h21WDzURR5NKl\nS+zbtw9BEOjatSutWrWSJeOdweyVni5Ny0RESLXjhw2Tqg+p/EH7YSj1HktNTWXXrl3s2rWLPXv2\nUKVKFTp37sxTTz3FU089hb+/vyy5GTQnrxwnr4qY/OOwqW7D9IDpnH/1PI0cGhH0vyBybucYrH0l\nxeWMjTH7WlQE330nVRzVFzVci0dpFASBZs2a8fLLLxMYGEhoaCjz58/nxIkTFBYWGvRcJsPJCf78\nE1avln6fMEEqTPD669JIv7j4gY9oMXn9KNVSUFDAnj17mD17Nm3btqVly5Zs3ryZHj16cOzYMZKS\nkli3bh2zZs3i6aeffqKDV9P10NAP1cTkn4SVYMXCZxfyxvY36L+yP7vH76Z29dpyy9K4w/79Umnx\nTp3kViIvgiDQvHlzmjdvzpUrVzh69Ci7d+/G1dUVLy8vvL298fDwUE8tb0GAjh2l4/PP4cIF2LgR\n3nkH4uNh0CAptW3fvtK2Cg29iI2N5c8//2TlypU0bdqUoKAgFi5cSEBAgGXkYdDQG9VP19+PTtQx\nZdMUUvNS2TJ6C9WqyJcjXB+UOi1YWT77DLKzpSynhkbtNissLCQpKYn4+HgSEhK4evUqLi4ueHl5\n0bJlS+rXr2/Q85nMXleuSCP9DRsgPFxy9C+8IDl9lTkmOe4xnU7H0qVLWbRoEcnJyUyePJnJkyfT\nSAWLHfXeJ3+6h7EkmQwbvwOKmq43OycPUKwrJuh/QTzb9Fne6vyWgZSZBrU7rEcxaRJ07w5Tphi+\nbXOzWVFRUZnTP3XqFJ06daJr164GW6wni73S02HLFli2DFJSpMp0Y8aoxtmb2mYFBQVMmDCBxMRE\n3n//fYKCggyyvc1U6O3kw83AybdTlpNXfUz+YVS1qsr8/vOZe3gu6TfTK9WWJcWXjNnX69crn+FO\nDdfCEBqrVatGw4YN6dmzJy+99BLR0dGsXbv2gf32arBHGU5OMGUKwZ98AosXw6+/SoUMQkMr3bS5\nxeRv3rzJgAED0Ol07Nu3jwEDBnDokHGSgGkxefPHLJ08QEunloz2Hc2H+z+UW4oG0uDNBDt3zA4b\nGxtsbGywsrJS/fbQMgIDIThYSoP7669yq1EUBQUFDB8+HA8PD37//XestXUMGpXELKfrS8m6lUWL\n+S04POUwzRybGaRNY2NuU8+lODtL++Pd3AzftrnaLC8vj61bt1JcXGzQSnaKsdeyZbBiBezZY9h2\njYApbCaKIhMmTCAvL4+1a9eqekGdNl2vTdebhLo16zKm9RhWn1sttxSLJj9fSoTj4iK3EnUgiiJh\nYWEsWLAAR0dHRo4cqeov/Ieyb5+0n/Jf/5JbiWJYsmQJ4eHhrFq1yvyut4ZsmLWTBxjaYijbY7br\n/XlLii8Zq6/Xrkkh2crmfVHDtaiMRp1OR0REBIsWLeL06dOMHz+evn37Uq3aw3eIqMEe9xO8Ywd8\n9BGMHCllRerdu/JtmkFMPjExkf/7v/9jzZo1D93brqY+qvG+NGfM/nHRw86D9PzKLb7TqBz16kmF\nykTRLJOhVRqdTsepU6c4evQoNjY29OzZk6ZNm5pPDL6UP/6A116THPupU4bNbaxy3n33XV555RVa\ntWoltxQNM8OsY/IA4anhjFw3kujXog3WpjFRTLzUwNSpAzEx4Oho+LbVbrO4uDg2bdrE8OHD8fT0\nNPr5ZLHX2rVSgpxly6S9lCrDmDY7efIkgwcP5uLFi9jY2OitUUloMfl7Y/Ix7/g/8XNNvgrTYvL6\nsDl6MwObDpRbhsXToAEkJcmtQplYWVlhZ2dnEgcvC6mp0gj+999V6eCNzcyZM5kzZ47ZOHgNZWH2\nTv5o4lF6Neyl9+ctKb5kzL66ukJaWuXaUMO10Efj9evX9fqCV4M9EEWYOlUqZNOpk6piwKawb3p6\nOmfPnmXy5MmyaFHT9dDQjyfG5AVBqAEcBKrf+ft1oih+IgjCq8AMoBHgJIpi1l2f+QHoD9wEJomi\nGH7n/YnAe4AIfCaK4nID9+cBojKiaFGvhbFPo/EE6tSBrKwn/50lEhUVhY+Pj9wyjMMff0BCAqxb\nJ7cSRRIZGYmPj4+qstkZlXgzW4eiAJ7o5EVRLBAEoacoivmCIFQBjgiCsB04DGwBgu/+e0EQ+gON\nRVFsKghCJ2Ah8JQgCHWAD4H2gACECYKwSRRFw5WMewg6UUc1K/3z1wcGBhpOjMIxZl9btJAqklYG\nNVwLfTQWFBToNZJXgz04fFjKZXyn4I4xNBvLDqawb2m9d7m0qOl6aOhHuVbXi6KYf+dljTufEUVR\nPAMgPLgEeAiw/M7nQgRBsBcEwQXoCewqdeqCIOwC+gFrKt2Lx+Bi40JIcgheDl7GPI3GE/Dzg19+\nkVuFMrG2tubWrVtyyzAOmZnQpo3cKhTL/v376a6tUygj9Wiu3BLMjnLF5AVBsBIE4TSQCuwWRfHE\nY/68PpB41+9Jd967//3kO+8ZlR/6/cCr217laOJRvT5vSfElY/Y1MVEqNV4Z1HAt9NFYWFioV2lZ\nNdiDESNgwQIpNo+6YsDGtu+6desIDw9/YjzemFrUdD009KO8I3kd4CcIgh3wpyAIrURRPP+IP79/\nZC8gxeAfFmx56P6SSZMm4e3tDYCDgwPt2rUrmwIqvYHK+3vB5QJmu89m0OpBTPOfRteSrtSqXqvc\nnw8PD6/Q+Sr6+/fff094eHhZf/XFEDYrxdB9DA4OZu1aePHFyrVXipJsZggbVq9enZUrV9K5c2ee\neeaZcn8+PDy8XO0HBwfz22+/AVTKZnrZa8gQ+PZbgjt0kBbgNW9e7v7J+fv333/Pjh07Ku2wHmWz\nnJwcpk6dyr///e+yUI0cfS7vPfSk3+++xzSURYX3yQuC8CGQJ4rif+78Hgt0KF14JwjCQmC/KIpr\n7vweBfRAmq4PFEVx2sP+7q72jbJ/OTEnkff2vcfu2N183ONjJvtNpnqVio+ejI3a93w/jLQ0KSaf\nkAB2doZvX+02KywsZOfOncTFxTFkyBC8vIwbWjK5vYqKpEI0c+ZIW+jmzIFm6qglUYqhbfbuu++S\nmprKkiVLDKJPaei9T35joJEUmQ6bYcHq2icvCEI9QRDs77yuCfQBou7+E+4dpW8GJtz5+6eAbFEU\n04CdQN87Mfo6QN8775kED3sPlg9bztbRW1l3YR1NfmjCjyE/kl+U/+QPa1SK//wHRo0yjoM3B6pX\nr86gQYPo27cvGzduZOnSpVy8ePGB0rKqpVo1mDYNLl4EX1/o2hWeew5CQuRWJhtbtmzhlVdekVuG\nhgVQnpi8G7BfEIRwIATYKYriNkEQXhcEIREprn5GEIRFAKIobgPiBEGIAf4LTL/z/nXgU+DknXY+\nEUUx2+A9egL+7v7sHr+b9SPWsz9+Pw3nNWTOgTkk3Xh4phZLii8Zo69Xr0rlw99/v/JtqeFaVEZj\ny5YteeONNwgICGD//v38/PPPnDhxgvz8hz+IqsEe92BjQ/DTT0NcHPToIeWv795dSpJz86bezRrL\nDsa076PqETwKNfWxMm0KgqD6Q2mUZwvdOaRtb/e//yPw4yM+89oj3v8N+K1CCo1EQP0ANozcwPn0\n8/wQ8gNtFrShU4NOvOj3IoOaDaJG1RpySzQLFi2SRvH1jb7E0jywsrLC19cXHx8f4uLiOHXqFHv3\n7sXT05PWrVvTvHlzvRbpKQobG3jjDZg+Xdo//9tv0kh/4EAYPRqeeaZsy5250rZtWw4cOEBAQIDc\nUjTMHLPPXV9e8ovy2XBhA0tOL+HctXNsGLGBbl7dTK5D7fHluxFF8PaGTZugXTvjncecbPYwCgoK\niIqKIiIigsTERPr370/btm31bk+R9rp2Tcpvv3o1REXBV19J++sVgqFtduTIEcaPH8+pU6dwcHAw\niEYloa+9bv7Z01iSTEbtofvVFZO3FGpVq8W4NuPYPX43NarUwMHa/P7jmZrz56FKFeM6eEugRo0a\ntG3blrFjx9KjRw+Sk5PllmR4nJ3h1Vel5DkHDsB778Hu3XKrMhpdunRh8ODBBAUFcePGDbnlaJgx\nmpO/C1EU+eLwF9S3q09rl9aACuOelcDQfT161LD1SNRwLYytUafTUbVqVZOcyxiUS7OPD6xZA2PG\nlO2vr3SbemBM+wqCwHfffUdAQAB+fn78+OOP5OY+OhGMmvqoxvvSnNGc/B10oo7Zu2ezOmI1G0Zs\nkFuOWVBYCLVry63CvCgpKSlz8mZN9+5QUmLWBQ8EQeDHH39k+fLlHDx4EG9vb95++20uXLhgPjsr\nNGRHi8kDRSVF/GPLP4jJimHL6C3UrVnXpOe/G0XGS/VkwQI4e1b6aUzMyWaPQ6fTsXz5ctq0aUP7\n9g+shS03irdXfj588om0IC8qSqpuJDOmsFlCQgI//fQTq1evpqCggC5dutC1a1e6dOmCv78/1tbW\nFdYtF/raqzyxa6Vzf2xdi8nLzO3i2wxbM4zM/Ex2j98tq4M3N6ysQKeTW4V5kJeXx969exFFkXbm\nusjh9m1Yv17KdZ+YCOfOKcLBmwovLy+++uorEhMTCQsLY/To0SQlJfHmm2/i6OhI586dmTVrFufP\nPyrZqIbGg1i0kxdFkSmbpmBd1ZqNIzdSq1qtB/7GkuJLhu6roZ28Gq6FoTQWFRURExPDrl27WLBg\nAT/99BPXr19n+PDhWFlZGfRcpuQBzQUFsHUrTJgAbm7w448wbx6sWiUtxtOnTQMhp309PDwYOXIk\n8+bN4+TJk6xbt47PP/+clStXcuTIEYOdR4vJmz8WENx7NN8d/47Y67Hsn7ifalX0L0er8XCsrKSw\nqsaTKS4uJiUlhYSEBOLj40lKSsLV1ZVGjRoxaNAg3N3dy5y7WZCbC7NnS/XmfX2lQjZffik5eo0y\nRFHkypUrnDhxgqysLGxtbZk0aZLcsjRUhEXH5N/Z/Q621W35oMcHJjlfeVB8vLQCrF0rLZJet864\n51GjzQoKCkhKSiIhIYErV66QkpKCk5MTnp6eeHt74+3tTY0axknIJLu9EhPh2WchIECKvasgU5Ip\nbCaKIgkJCYSFhZUdp06domrVqvj7++Pv78+oUaNo2bJlhfWbGi0mr5yYvEWP5Ds36MziU4vllmG2\nuLtDZKRUn6SCWTzNitu3b5OWlkZqamrZkZmZiZubG15eXjz99NN4eHgYzakrjr59ITVV+rl9u1S9\nqHlzqFcPFJgW1BjcunWL8+fPc/bs2bIjPDwca2vrMof++uuv0759e9zd3eWWq6FiLNvJe3RmyuYp\n6EQdVsLDp0KDg4PLSiuaO4bua+fOUsa7Dz+Ezz+vfHtquBZbt26lSZMmpKamljn2mzdv4uLigouL\nC/Xr16d9+/a4ublVeiucGuxxP8HBwQRu2wbh4RAdLSW/+eUX6bUg/O3wS48WLaBRI3jMA5Cx7GCI\ndktH53c787Nnz5KQkEDz5s1p3bo1bdq0oV+/frRt2xZXV1ejaTFVu5WJyYfkfmk4IbLRR24B92DR\nTt7VxpUaVWqQkptCA7sGcssxO6ysYPly6NhRmqGdOxc8PeVWZRwKCwvZunUru3btolu3bri6uuLj\n40Pv3r2pW7euecXTK0ujRtJxN6II6enSlrnoaOk4ckT6vbRGsbu7NLXv7n7v66tXpQcCZ2cpxaKC\nGDp0KMePH8ff3582bdowdOhQPvzwQ5o3b17hIjUaGvpg0TH528W3qfdVPVJmpmBXQxl1UGWPlxqB\n3Fz4+mv46Sd4+WX45z8NW3ZWLpsVFhaSm5tLVlYWu3btokGDBgwYMEDxX96qu8d0OsjIgJQU6UhO\n/vv13b9nZYGT070PAnc/DHh6goeHVCCnguhrs/fff58PPvhA/UWFKoi+9hreYULZ7y3d29KqvvK3\ni55PDudCypmy3zecXK7F5OWkqKSIPbF7+D3ydzZHb6ZXw17YVreVW5ZZY2sLc+bA1KnwwQfQrBl8\n9BG89BIoMXlbUVERubm59xx5eXkPvKfT6bCzs8PGxoann366UkVjNB6DlZU0Snd2fnwhhKIiKdZ/\n/wPAgQPSz8REuHIFrK3/dvieng++dnc32I1pbW1NTk4OTk5OBmnP3HkuYKLcEipMq/rt7nkY2XBy\nuUHaFQRhEHBWFMWEO79/CDwHJABviqIYV552FPgVa3hEUeR40nF+C/+NDVEbaFq3KaN8R/F5789x\nt338ohY1xj31xdh9bdAAli6F06dh1iz44QdpdN+rlzz6bt++zZEjRx5w3kVFRdja2pYdNjY22Nra\n4uzsfM/7NWrUeKB+tCnvFzXem8aKAQcGBkqrOz08pONRiCJkZv7t8K9ckV6fPv3367Q0cHEh2N6e\nQF9f8PLSW1tsbCzNmjVj6NChtG3bFicnJ+rVq0e9evXKXteq9WB+jkf20cAoLSavcQ+fAU8BCILw\nLDAOGA34AQuBoPI0YtZO/vqt66w4u4LFpxZzu/g2L/q9yImXTuDt4C23NIvGzw/27JFyoIwfD5Mm\nwccfy7MCPy8vjwsXLiAIAv7+/nTs2BE7O7sHnLeGmSAI0ir+evWgbVtISpLi/xcvSoe9vfQ3V69K\nf3/zZqWSPfz666988cUX/O9//yM2NpbQ0FDS09PJyMj4f/bOOyyKq/vjn6UoipUOVgQbiKKAUVGx\nC/YWe16jMZYkRpNfiqYXE2PKm0Sjr4qmWGNUNPYuioqiiAVRFLGDgIJUkTa/P27AEgtld2d2mc/z\nzLNL2TvfOTM7Z+49957D7du3SUpKwtTU9F+O/+H3tra2XL9+HTs7O2xtbbGyssJUYXMPVHSCJElS\n1j/vBwFLJEkKB8I1Gs1rxW3E6GLyV+5eYXfsbnZe2snOSzvp1bAXE7wm4FfPzyBu3AYXLy0jiYkw\nZgzcuwd79pRu3lRZbZabm0t0dDRnzpzh6tWrtGvXjo7aLJ+nMMrNNZaXJ5x1Ya/9+vUH26VLEBMj\n0uY2aiQm7jVq9GBzdn7kqVNXNpMkiczMzCKH/7Dzf9r7u3fvUr169aKHADs7u6LVGw9vDg4O2Nvb\nU6UUcxDKSmnttWLyHl1J0huj/tdVKzF5jUZzGmgHZAGXgcGSJB3/529RkiS5FUePwffkk+8ls+/y\nPnbF7mJ37G7Sc9Lp6tyVni49md97PjaVbeSWqPIM7Oxgyxbw9RVpy4cO1b8Gc3NzmjVrRrNmzUhL\nS2PBggV4eHhQsxzlTTc4CgrEbPwnOfDCLSFBTMQrHMKvU0c4744dwcUFXF3FhBEZ0Wg0VKlShSpV\nqlC/fv1ifSY/P5+UlBSSkpKKtsLlmidPniQhIaFou3XrFhqN5l/zDBQZAAAgAElEQVSO/1kPBIbQ\nGSon/AScBNKAcw85+JZAfHEbMUgnH5Mcw/pz69kQvYEzCWdoX7c93Rp04zWf12hm1+ypa95LgyHG\nPUuLXMdqYgIffAAzZz7byetDX7Vq1WjTpg1LlizB0dERW1vbR7bnJaxRY/LPpsSaMzPFcPq5cw+2\n8+dFT7xqVahTh+BKlejUsqVw4q1aPXDoTk5QhlntSrLvw1oeHt5/XvY7SZLIyMj4l+NPSEjg9OnT\nREZGUlBQ8MgDQZs2bejRowc9e/akefPmJXb6akxeO0iS9KtGo9kB2AGnHvrTLWBscdsxGCd/6tYp\n1p1bx/rz60nKTKJ/4/583PFjOtfvTEWzcpIpzIjx9xfx+X/mPMlKhw4daNasGUlJSSQmJnLlyhXC\nwsK4ffs2lpaWjzh9e3t7HB0d1d5PWZAksUTucWd+7py4IBo2FElxmjaFF18Urw0bQuGEteBgUIgz\nVhoajaZooqirq+u//v74g8zdu3c5cOAAO3bsYMiQIaSnpxc5/J49e2Jjo46M6guNRtNEkqTzGo3G\nHvB8wj3mWrHaUVqc7eE4liRJbIvZxjcHv+Fq6lWGug1lYNOBtKndRqu9dSVRbuKlT2DsWBEGXbiw\nZNlN9WWzgoIC7t69W+T8o6OjiYuLY+rUqVSvXr1EbcmJbNdYcjJcvPjkzcRExMKbNn10c3ZWRIKb\n8vq9jI2NZefOnezYsYN9+/YREBDAxIkT8fN79hwnNSavlZh8oCRJr2o0mn1P+HdJkqRirUtSrJPf\ncH4Dn+z7BBONCe/7vs+L7i9iZmIwAw+lprzeTEAkzWnfHoYMgQ8/FPf94iCHzW7dusUff/xBQEAA\nzZs3L3U7cqBze2Vnw65dInXtxYti1vrFi2Ide8OGD7ZGjR68t7YuzaHojfL8vSwkJSWF5cuXs3Dh\nQnJzc5k4cSKTJ0+mUqVK//pf1ckrJxmOIrvDh68fZtLmSXzb/VsiJkYwwmOEbA6+PMWX5D7WqlXF\nJLwtW8DLS/iJh5Fb38Pcu3cPW1tbNm/ezJIlS9i1axcXLlxgx44detOgJHuQnQ0bN8Lo0aJc7H//\nK5ZMdO4M338PUVGQmkrwDz/An3/Cl1+K+EybNmV28MZYT/5xlHCMNWvWZMqUKZw5c4Zff/2VkJAQ\nPD09OXjwYKnbVHk6Go3GR6PRODz08380Gs3fGo1mjkajsSpuO4rsGv9n/X+Y33s+/q7+cktR0TO1\na0NoqJhp/9prYrR29myxtl5JODs74+zsTG5uLjdv3uTq1ascOXKEkJAQLl++TNOmTenYsaPxx+rv\n3YM33oCgILHufOhQ4dSfUmhFxfDRaDT4+vri6+tLUFAQQ4cOZdCgQcyaNYuqMq9WMDIW8k+1G41G\n0xH4BpgCeAKLgCHFaUSRw/XN5jdjdrfZ9GrYS245ekcdFnxAbi4EBoqUuN27i87fk1YZKclm+fn5\nxMfHs3HjRnx9fRWZ6lZr9pIkGDcO0tJg7lwxm91IUdI1pjRSUlL4v//7P/bs2cPChQvx9/dXh+u1\nE5M/JUlSi3/ezwOSJEn67J+fT0qSVKzE/orsyX/m9xkf7v2QHi49ykUcXuXJmJuL3vxLL4nOoZeX\n6NWPHy+3sqdjampK7dq1GTRoEMuWLaNevXrUqFFDblm6ITAQwsLg6NFSFX1RMQ5q1qzJr7/+ys6d\nO5kwYQIff/xxqduaXVv+SZYKwlSj0ZhJkpQHdAUmPPS3YjtGRcbkBzUdhFUlK34+8rPcUspVfEmp\nx1q1Knz+OcyZE8xnn8GKFXIrejqFNnRwcKBt27Zs2LABXfXmZD1fx46J2ZFBQSVy8LrQrIR4ta4x\nhGPs0aMHvXr14uTJk1prs5yzCtiv0Wj+Bu4BIQAajcYVSC1uI4p08hqNhoV9FvL1wa+5mXZTbjkq\nCqFWLdixA95+WxQWUzrt2rUrSplrVNy+LdarL1gg0sGqqPxDTEwMtWvXLn0DGo3hb1pCkqSvgP8D\nfgfaPxT7MUHE5otnUqXFjB6OY03fPZ2kzCSW9F8isyr9ocb+ns+OHSIUfPy4mMitZJvt3bsXExMT\nxWROgzLaKy8PeveG5s3hu+90JVFxKPkaUxL+/v5MmzaNgICAUtmr+dcG8PT+HE5/4KetmPwgSZKC\n/nlfU5KklNLoUWRPvpAZ7Wew+eJmziSckVuKioLo2RNGjRIT8pSOi4sL4eHhxMcXO9W0cjl9Wix5\nq1gRZs2SW42KAqlZsyYJCQlyyzAWPnrofalnJCrayVe3qM4H7T/gnV3v6Cyu+TyUFJfTNUo/1of1\nTZsmllunpcmn50k8bsN69erRq1cvVqxYwc2b2g096f18de0KkyfD33+DWekmxKox+dJhKMfo5eXF\nhg0btNpmOUbzlPclQtFOHmCyz2TuZN3h20Pfyi1FRUE4OYkCY1r2mzqhadOm9O7dmzVr1pCdnS23\nnNITEQGvvKLVuKOKcdGjR49/JcdRKTWVNBpNS41G4wVY/PO+VeFW3EYUHZMv5EbaDVoHtuaVlq8w\nruU4nGs6y6RO96ixv+Lj4CDi8nXqKN9mWVlZLF++HCcnJ/r06aO3/T6JUl9jBQXl1sGr38vi07p1\na44dO6bG5P+hDDH5J+WsL6TYuesNYhF67Wq1CX45mLlH5+IT6ENz++a87Pkyg5sOxrKCpdzyVGTC\nywv2PetrIDOSJBEbG0tERAQxMTE0btwYL6/nf9kVy6BB8PvvYEDFeFT0iyRJJCYmyi3DKJAkqbM2\n2lH8cH0hjawbMbfXXG6+fZPXfF7jr7N/Ufenuuy8tFOn+1VSXE7XKP1YH9c3ZgwsXy6PlqdRqDEt\nLY0FCxawe/du6tWrx7Rp0xg4cCCOjo5a35fesLGB4cPL1IQaky8dhnCMubm5fPrpp8afytnAMBgn\nX0hFs4oMcRvC5pGb2Th8I6ODRrMjRn9FQVSUQ0AAHD4st4p/k5aWxh9//IGHhwcTJ07Ex8cHCwsL\nuWWVnQEDRCpbFZXHOHPmDC+88ALHjx/n22/V+VNKwiBi8s/i8PXD9F3Vl1OTTlG7WhmSMCgENfZX\nMl54AcLClGWzP/74A2dnZzp27KiT9stKqa+xTz+F1FT48UddSVMs6vfyydy4cYMvvviC9evXM3v2\nbMaOHYtGoym1vdSYfDkpNVsS2tVpxySvSczYM0NuKSp6RpKKX3NeX9y4cYOUlBR8fX3llqJ9Fi+G\nsWPlVqEiM5IkER0dzXvvvUeLFi2wsrIiOjqacePGqUP1CkRht8jSMdhtMMduHtNJ20qKy+kapR/r\nw/oOHoSOHUXHUkksW7YMb29vTE11X2hD7+erUSOR6a4MqDH50iH3MWZlZbF161amTJmCq6srXbp0\n4d69e5w+fZpvvvkGK6sH5c2VZDcVA5ld/zwOXD2AXz0/uWWo6IGoKHjvPYiMhM8+ExXqSpmXRSfE\nx8fj6uoqtwzdMHu23ApU9ER2djbh4eGEhoayd+9eQkJCaNWqFQEBAaxfvx4PDw+1124gGHxMHqDf\nqn6M8hjFsGbDdKRKf6ixvyeTnCyc+qpV8MEHogRtxYrib0qy2ZIlS/D19aVJkyZab1tblNpeMTHg\n4qIrWYpGSdeYLrhx4wahoaEcPnyY0NBQzpw5Q9OmTWnbti0dO3ake/fuJSqZXFp7LTeCevKjtVRP\nXlsoqA9UekKuhbCo7yK5ZajoiPXrYdIkGDwYzp0TK7mUire3N4cOHcLFxQVzc3O55WiX6dPh119F\n7V8Vg0WSJGJiYggODiY4OJgDBw6QnZ1N27ZtadeuHbNnz8bb2xtLSzUHiTFgFE6+omlFCqQCnbQd\nHBysqApiukRpx5qfD59+CkuXwqZNkJUVjI1NJ7llPZM7d+5Qo0YNVq9ezfDhwzHTYSxB7+erenXw\n8IBFi6BHj1I1oQvNurKDkr4PZdFSmJRp3759RY4doHPnzjg5ObF3715cXV21Nvxelpj8gB4ztaJB\nVv4nt4BHMQon39y+OW9sfYNvun1DI+tGcstR0RKTJ8P58yJ1rZ0dGMJ8HhMTEwYOHMiGDRv47rvv\nsLKywtraGisrq6LN2tqaypUrG15Mc/Fi2LkTJkwACwto3/7B5uJSblPeKpVLly6xatUqVqxYQWpq\nKp07d6ZTp0589tlnuLi4oNFoCA4OpmHDhnJLLeJWqMIqThkBRhGTz8zJZM7ROfz3yH8Z0HgAH3b8\nkPo16utGoI4x9thfcVm0CH7+GY4cef7osFJtdu/ePZKTkx/Z7ty5Q3JyMgUFBY84/kLn7+DgoPNh\n/jLbKz9fzHw8ePDBlpv7wOF36gQtWhiV01fqNfY4d+/eZfny5axYsYLY2FiGDh3KyJEjadOmjV4f\nKktrr+LErpXOE3LQyxqTNwonX0jKvRS+PfQtgScCaWrblJHNRjLEbQi2lrZaVqk7DOVmokvi4qBZ\nMwgNhcaNn///hmizwgeAQqefnJzM7du3uX37NjY2NtSqVYvatWtTq1YtrK2ttXqD1om9rl174PB3\n7BA9/TFjYPRoUTLQwDGEa+zIkSMMGzaMtm3bMmbMGLp16ybbvJDS2itjfScdKdIfVQYGK8rJG8U6\n+UJqVqrJrG6ziPu/ON73fZ+QayG4znWl14pehFwNKVWb5WnNp1KO9csvYdy4fzt4peh7FsXVWKlS\nJWrVqkXz5s3p1KkTgwYNYsKECbz33nsEBARgZWXFxYsXWbFiBd9++y3Lly8nODiYrKysEu9LL9St\nCyNHwvz5EBMDCxbAhQvg7i7yDx85Aqjr5EvL87QEBgbSr18/5syZw59//klAQECxHLwhnQ+V0mEU\nMfnHqWBagT6N+tCnUR8ycjJYFL6It3e+zbFXdZMwR0V7XLoEa9ZAdLTcSuTBzMyMOnXqUKdOnaLf\nZWRkcPPmTaKjo1myZAkjR47E2tpaRpXPQaOBDh3E9vPPwuFPmQLH1O+frjhy5AgjRoygf//+cksp\nEwY3T8UAMKrh+qdxJ+sODj84kDo9lcrmlbXatrYxhGFBXfLSSyKx2scfF/8z5clm27Zt4/Tp00yc\nOLFE65YfRu/2On4c2rSBy5fhoYcXQ0Lp19itW7fw8PBgzZo1ilgRUFp7ZW7QSnVVWbEcsE9Rw/VG\n2ZN/mPj0ePqs6sNYz7FUMqsktxyVZ5CUJJbKXbkitxJlkZ2dzZkzZzhx4gT37t2jTZs2VKlSRW5Z\nzyY3VyQ4mDtXnNAvvwR7e7lVGS0ODg6sWrWKESNG8PrrrzNjxgy9pFZWUT5GFZN/mMTMRGaFzMI7\n0JuBTQaysM/CUg0Flaf4ktzHuno19OsHT+ugyq2vOGi7PvfevXv5+eefuXLlCt26dWPq1Kn4+flh\nZmamTHskJcFXX4GzM8ybB1Onih78jBlQoYJBxYCVZN/iaOnWrRvh4eHs3r2bgQMHcv/+fa20W1KU\nZDcVI3TyoddDGR00msa/NCYmOYZNIzbxUceP1FiPARAVBT4+cquQH0mSOHfuHPPmzSM5OZnJkyfz\n4osvFq1tViQnTsDLL4tYy+XLsGUL7N8PQ4Yoq7iAkePk5MSuXbswNzdn2LBh5Obmyi1JRWaMKia/\n+MRiPt//OW+1eYuXPV/GqpLV8z+kMJQe+9MlI0dCt25iZn1JMBabFRQUEBUVxeHDh8nLy8Pf358G\nDRpofT9atVdKCrz9NuzaJSbXjR8PSp4UWEoM7RrLycnBw8ODn376iYCAAL3vX43JqzF5rXM64TQz\n9swgZGwITWyUWxxE5en4+cHevSV38oZOQUEBx44dIzQ0lOrVq+Pn50ejRo2U22svZMsWkf1u0CCR\nmlDp8wTKEWlpaSQkJODr6yu3lBIh1dNNevLyjNEM1/99/m9eav6S1h18eYovyX2sAQEij0rBU77n\ncusrDqXRePDgQU6fPs2QIUMYO3YsjRs3LpaDl9UeYWEwdqwoCzh3brEdvCHFgJV0vZVUS25uLhYW\nFixdupRnjSYY0vlQKR1G05OvXa02Z5POyi1DpQzUrSty1IeHl5/YfFxcHGFhYUyYMIFq1arJLad4\nZGfD8OEi8U3HjnKrUXkCjo6OhIaG0rdvX2JjY/nvf/8rt6TicVXho1cGiNH05P1d/Tl47SDrotZp\ntV0lrDnVF3IfqyRBejpUespKR7n1FYeSaiwoKMDMzKxUZT1ls4eJCTg4QEiIOGklQBeadWUHJV1v\npdHi7OxMly5dSElJ0Wq7z0NJdlMxop68Y1VHNo3YRM/lPXGs6ki7Ou3klqRSQsLCoHJlkQm1vFC7\ndm1sbGyIiIjA29tbbjnFo0IF2LoVunQRWYtmGkF5UCPk0KFDrF27lsjISLmlFJ96ypoMawwYTU8e\noKVjS/4Y8AeDVg/i4p2LWmmzPMWX5D7WNWvgxRefXrxMbn3FoTQaO3XqREhICHl5eTrfl9aoUUNM\noAgKgtmzi/0xQ4oBK+l6K6mW7OxsXnnlFebOnYuV1dNXGRnS+VApHUbl5AECGgbwRecv6LWyF7ez\nbsstR6WYSBKsXSucfHmjdu3a2NnZERERIbeUkmFrK5bOLVokEt+oKIbPP/8cDw8PBg8eLLeUkqEx\ngk1hGNU6+Yf5YM8HBF8JZs9/9lDJ3HDS2RraelxtcewYjBolCtOUdOWYMdjs5s2b/PXXX0yZMgUz\nHSeP0bq9Ll8W6x+//FKUlzVCDOkaCw8Pp1evXpw+fRp7mVIJl7rU7Ek/XUnSG1U89ytqnbzR9eQL\nmdllJvVr1Oel9S9RIKlrL5VOYS9e6UvDdUWtWrWwt7fnxIkTckspOc7OsHMnTJ8uTqSKbOTk5DBu\n3Dh++OEH2Ry8irIwmol3j2OiMeG3/r/Rc3lP3tz2Jt/3+B4LM4sStxMcHFxuZovKdawFBcI3rHvO\nwghDOBel1Zifn0+TJk0IDg6mVatWxerNK8oeTZrAtm3QsyfEx0O7duJ3j60a0IVmXdlBSfZ9nhZJ\nkoiNjeWnn36idu3ajBo1SivtloayxOT/XliC8pOKZb/cAh7BaJ08QEWziqwftp4xG8bQcG5DPmj/\nAeNajqOiWUW5pak8xMKFYn18ixZyK9EP+fn5JCUlERcXR1xcHPHx8SQmJmJlZYWrq+szk5coGk9P\n2LwZfvgBFi+GCxfEUjs3twdbdjZ4eUHVqnKrNWgKCgqIjIwkJCSEAwcOEBISgkajwc/Pj8DAQOVn\nSyxnOLaTLweG0cbkH+fYzWN8GvwpZ5POsmLQCtrXba/1fWgDQ4r9aYPz56FDB1HLxM2tdG0Ygs0K\nCgo4dOgQ0dHRJCYmUqNGDZycnHB0dMTJyQl7e3sqVKigFy16s1denojXR0U9up0/L/Lbu7kJh9+p\nk+j5lyJXgL6Q+xqTJIno6Gh27NjB7t27OXToEDY2NnTo0IGOHTvSoUMHnJ2dFePcS2uvFZP36EqS\n3hj1v67/iskXJyf/4znvtYVR9+QfxqeWD1tHbWXD+Q2MChrFqUmnqGHxlJqmKnohKgq6d4f//rf0\nDt4QuH//PkFBQdy/f5/u3bvj6OioN4cuK2Zm0LCh2Pr3f/D7ggK4ehXOnoWjR+GLLyAiAjw8hMP3\n8wNf33Lf209NTWXPnj3s2LGDHTt2kJ+fT8+ePXnppZcIDAzEwcFBbokqBkC56ck/zOTNkzE1MeWX\nXr8893/1HZeTs8egz2ONjwdvb7HEevTo4n3mafrk7mU9zJM0rlixgkqVKtG/f39MTU11uq/ioMhr\nLCsLjhwRQzr798Px42L4f8AAGDgQXFxK3qYWterTZocOHWLWrFns37+fdu3a4e/vT82aNRkzZozW\ne+q6isl37ty5VPZq/lWwVrXIwekPO6k9ebn52O9jms1vxuxus7GsoNwhQmNFkkTxsldeKb6DN2Rc\nXV2Jjo7GxMRoF7OUncqVRQa9Ll3Ez9nZEBwM69eLoXwHB+HsBw0SPX6FDEtrC0mS2LdvH19++SVX\nr15l+vTp/PXXX1SuXBkQjlMpQ/EqhkW57MkDdFvajakvTKVv474631dJUFKvVFds3Qrvvy8K0Whj\n1FrpNisoKOD333/nzp071K5dm1q1ahW9Vqyo/0mgSrfXv8jPh8OHhcNfu1bEdn78EZo21ZsEXdts\n6dKlfPLJJ3zxxReMHDlS57kSdE1p7bXcCGLyo9WYvDKoXa22mhFPJhYuhLfe0o6DNwRMTEwYN24c\naWlp3Lhxgxs3brB//37i4+OpWbNmkdN3dHTE1tbW4G/wWsfUVMzO7NBBxHd++UVUvxs9Gr76SowC\nGDBZWVl8+OGHrFmzhjZt2sgtR8XIKJfjhwVSAWcSz2BT2ea5/1ue8jDr41jT02HvXhg6tOSfNYRz\n8SyN1apVw83NjR49ejB27Fjef/99+vfvj6OjI1evXmXDhg3Mnj2befPmsXbtWkJCQoiOjubu3btP\nXFZnCPZ4nDJrNjcXT4hnz8KJE7B0qcHnrr98+TKmpqbPdPCGdIxlaVPujLRGmNW2/PXkJUli/rH5\nAPRu1FtmNeWPkBBo1QqqVJFbifyYmpri5OSEk5MTPj4+gFhDf/v2bRISEkhISOD48eMkJCSQk5OD\nnZ0d9vb2ODg4UKtWLfLz82U+Ahmxs4OXXxaZ9po0kVtNmWjSpAmpqakEBgbi7e2Nm5ubLGEcFeOk\nXMXkD18/zDs73yErN4vfB/yOp4OnTvZTFgwuXlpCCtfFBwdrr6SssdsMxJBuYmIit27d4tatW9y8\neZPU1FTs7e2LHhScnJywtrZ+7gQ/o7HXN99AbKwokKNjdG2zdevWERQUxMmTJ4mNjaVhw4a0aNEC\nT09PWrRoQYsWLbC1tS2VdjlQ18mrMXm9cvTGUWYdnMWJ+BPM7DKTUR6jMDXR3lImleLTpIlIiDZo\nkFgxVbOm3IoMg8qVK1O/fn3q169f9Lv79+8THx9PXFwcERERbNiwgQoVKlC/fn1GjBghn1hdUlAA\nZ86IHvycOUaTK3/w4MFFFeOys7M5e/YsJ0+e5NSpU2zcuJFTp05haWmJm5sb9vb22NjYYGtr+8RX\nKysrrS7VVDFsjNbJF0gFbLu4jW8Pf8vVu1d5u+3brBq8qsQV6ZSUv1rX6OtY//MfOHVKLIHeuROK\nOzJpCOdCHxpzc3OJi4tj06ZN2Nvbc+PGDXJzc3F1daVWrVqPPAgojVLZJy5OlLQt3KpXF1mUFi+G\ndu2MLne9hYUFXl5eeHk9qFy2b98+nJ2dOX/+PElJSdy+fZukpCSuXr36yM9JSUmkpqZSo0YNbG1t\nn/ogUPgaExND3759i5bqaQNDnCtizBiNky+QCjibeJaD1w5y8PpBDlw9gHUla973fZ8X3V/EzMRo\nDtUo+O47GD4cpk6FBQvkVqMc8vPzSU9PJy0trWgr/LnwNTMzEzs7OzIyMujYsSNdunTBysrKsNdR\nSxIkJYl4zrlzj76mp4v18z16wMyZoOCHGF2h0Wj+NZLzNPLy8khOTi5y/A8/AFy+fJmwsLCi3924\ncYMxY8ZgZmaGs7MzLi4uuLq6PvJat25ddcWHAWOwMfn7efc5evOocOrXDhJ6IxTbyra0r9ue9nXb\n41vHl0bWjQzuxmc08dJikJoKzZrB8uUik2lpMSSbpaamcufOnac68Hv37lGlShWqVatGtWrVqFq1\n6iOvhe/LctOV3V7Xr4uhnIcd+fnzwtE3bSpiOoWvTZqIUrYyDz/LbjMdIkkSaWlpXL58mZiYGC5d\nulT0eunSJRISEqhTp86/HgDc3d1p0KDBE9tUY/LKickbjJOXJInIxEh2xe5iV+wuDl47SBObJnSo\n26HIqdtXMfz6ycZ8M3kSmzfDpEliIp6ra+naULrNsrKyiIyM5PTp06SkpGBnZ/dEx12tWjUsLS11\nnhlP7/ZKTxcneOdOsSUni8I0Dzvzpk3B1laxmeyUfo3pkuzsbK5cufKI44+JiSE8PBxvb29mzJhB\n+/aPFvxSnbxynLzix2A2X9jM6rOr2R27m8rmleneoDvjW45n5aCV1Kyk+1lbhhAH1hZyHGufPvDp\np9C1q0hZ/qzRSEM4Fw9rvH79OocOHeLKlSs0bNgQPz8/GjRooLVJUYq2x6VLsGqVcOoREfDCC9C9\nO8HvvEOnV14BLT7IGFtM/knIeYwWFhY0adKEJo8tVczOzuaPP/5gzJgxODk58d1339GmTRs1Jq8w\nFO3k15xdw9s73+bDDh/ymd9nuFg9vUiFiuHy6qtw7x74+4vspVZWcivSDrdv3+b69etYW1vj6upK\n/fr1jXvWc0GBmBg3d66oLjdyJHzwgchOVzixKzhYqw5eRT4sLCyYOHEir7zyCl27dmX79u1lzthn\n8YbX8/9J6fxPbgGPotjh+lO3TtFtWTd2vbRLkevZdUV5HhZ8911RfGzfvpJ9Tsk2y8/PJzo6moiI\nCG7evEmLFi3o2rWrrBOZtG6vnBz47TdRM7hSJZgyBUaMMPh0sw+j5GtMbo4dO0afPn24cOEC1atX\nB0pvr3Vn7+pEoz4Z7F5DHa4vDt8c+oYP2n9Qrhx8eWf2bOEj7t8v/rI6pWNqaoqbmxtubm6kpqay\nc+dOfv/9d4YNG0ZVQ6+XnpsLf/whZrw3aSKWtLVvr9i4uor2uXDhAv3792fBggVFDr4sGNpEaUNA\nkeNm6ffT2XxhM2NbjpVbSrmKL8l9rCYmUK2amJf1JOTWVxyepbF69eoMGTKEhg0bEhgYSEhICBkZ\nGTrZl17w9oY//4SVK2H7dpHK8Dk3aaXlSpej3dKgxGMMDg6mW7duzJw5k4EDB2qlTRXto8ievIWZ\nBffz7lOlgprgvDwRGytWStkb/iKJp6LRaPDz86Nhw4YcP36cefPm4ezsjJeXFw0aNDCsnkxkpBiq\nN+Z5Bir/IiMjgxkzZrB+/XoWLFhAnz59tNb2vbnHtdaWikCxMXnXOa4sHbiUdnXayS1Jr5TH2N/d\nu7B0KcyfL/KdzJlTss8bss3u37/PmTNnCA8PJysrCw8PD49n4zsAACAASURBVJo3b46dnZ3O9qk1\ne9naihMXEKBNeYrEkK8xbZCTk8OuXbtYs2YNGzdupG/fvvz000/UfEpeanUJnRqTfy7v+77PjD0z\nCB4TbFi9G5ViIUkQFgaBgbBuHfTsKerMd+wotzL9UrFiRby9vfH29iYhIYHTp0+zfPlyKleuTPPm\nzWnatOlTb6Sy8/ff0L+/iMsHBKixeCPj1q1bHD16lKCgIDZt2oS7uzsvvvgiM2fOpHbt2nLLUykm\niozJA4xtOZYCqYAha4aQdj9NNh3lKb6k62MtKBBFad55RyQx+89/wMVFJDv780+R9e5ZfsIQzkVZ\nNNrb29O9e3feeust/P39uX37NkuWLOGXX35h27ZtXLx4kdzcXK3sSyu0aycKxEyZAm5u8PXXcPXq\nMz+ixuRLhy6PMT8/n3PnzvHnn38yffp0/P39cXBwwN3dnV9++QUfHx8iIyMJCQnhzTfffK6DV5Ld\nVBTckzczMWP3S7uZun0qLyx+gdVDVtPcvrncslRKiCTBsWNibta6dWJi3ZAhsGmTSGmrdv7+zcN5\nyiVJ4tatW8TExHDo0CHWrl1LnTp1cHd3RxFDwX5+EBMDoaGwbJnIZOfuLsoMenuDpydYWsqtUuUf\n8vPziYqKIjQ0lPDwcA4cOMC1a9dwdHTE09MTT09PXn/9dTw9Paldu7Y6iiojGo2mHnBXkqTUf37u\nDAwArgK/SJKUU6x2FHGjeIgnxbGWnlrKOzvfYaLXRD7s+CEWZhYyqdM9xhL7i40VOemXLxc/jxwJ\nQ4eKDp+2MRabFYfs7GwuX77MoUOHsLCwoH///iVeiqdTe92/D1u3wo4dEB4OZ8+KYZtWrcQDgJeX\ncPwGtnzQUK+x1NRUjh49yuHDhwkNDeXo0aPY2dnRtm1bfHx88PT0pHnz5lSrVk2r+1Vj8mWPyWs0\nmqPAQEmS4jQajSewG5gFNAdyJUkaXxw9BuHkAeLS43hj6xucu32ObaO2Ub9Gff2L0wOGejMBuHlT\n9NZXr4aLF2HYMHjpJfDx0W2P3ZBtVlry8/M5cOAA4eHhjB07Fmtr62J/Vq/2ys0Vjj48HE6cEK9n\nzkCdOuDhAfXq/XurUaPk+9ExhnKNpaenExwczM6dO9m/fz+xsbF4eXnRrl072rZtS9u2bbG1tdW5\nDtXJa8XJn5Ykqfk/778HCiRJek+j0ZgAJwv/9jwUO1z/OE5VnQgaFsRPR36ix7IehIwN0UtBGiXl\nr9Y1pTnWQse+Zo24l/ftKzKZ9ugB5uby69M3+tJoamqKRqOhadOmnD9/Hl9fX53vs1SYm4ueu6cn\nvPKKsE/79qL63NmzIoZ/7pxYZ3/1qthMTZ/s/OvVE8UNHitkU55z1xcUFBAREcGOHTvYuXMn4eHh\ntG7dmp49e7JkyRI8PT0xf8YXURfHqMbktcbDD0ldgBkAkiQVlCSMYjBOvpBpbaZxJ+sOo9ePZtdL\nu+SWU26JjISPPoIDB4Rjnz4dunUznkx1SkeSJG7evElOTg7379+XW07JMDMTvXgPj3//TZIgJUU4\n+ytXHjj+w4cfvM/KEg8Nfn5iOUZBgd4PQSm0bduWO3fu0Lt3b9577z38/PywVOdAGAt7NRrNX0A8\nUBPYC6DRaByBYsXjwYCG6x8mNz+XOj/W4cDYAzSybqQnZfpB6cOCly+LqnHbtwvHPnmySEUrJ0q3\nmbaQJIn09PSidfUVKlTA29sbDw8PKpbg6crg7ZWeLtZfHjggShcePy7S6vr5ia1LF6ii3URaSrXZ\nxIkTqVOnDh999JFO91NS1OF6rQzXa4BhgCPwlyRJN//5fUvATpKkHcXRY3A9eQBzU3OGug8l6FwQ\n09tPl1tOueHvv2H8eHjtNTGhWstzdVQeIi8vj6SkJBISEh7ZJEmiUaNGDBo0iFq1apXP2c9Vq4ra\nxF27ip+zs8USjgMHxPrMIUPEcr5ywBtvvEHXrl0JCAjAy8sIKripFPHPE+KfT/h9REnaMUgnD+Dl\n6MWuWN0P1yspLqdrnnWs330HP/8MW7ZA69b61VWIIZyLkmiUJImsrCxSU1NJS0vj9u3bRc48JSUF\nKysr7O3ti2ZD29vbU7Vq1SLHbgj2eBydxICPHBFtNm8uZva3bauddhVk36dp8fDwYNGiRfTu3Zud\nO3fSvHnJlhmrMXnlotFoDkqS1F6j0aQDDw8JaRDPAMXqZhmsk3e3c+fHIz/KLaNccOMGzJolJkXX\nqiW3GsPh/v37RQ48NTX1kfdpaWmkpaVhbm5O9erVqV69OjVr1sTV1RVfX19sbGxkLUdrMNy5Ixz7\nd99BSIiYGNKli9yq9MqAAQO4d+8e/fr1Izw8vEQrLVSUiyRJ7f95LdN6U4OMyQNk5mRi850NqdNT\nqWBaQQ/K9IMSY38//CAc/O+/62wXZUIpNtu5cye3b98ucui5ubnUrFmT6tWrU61atSJnXvi+WrVq\nVKig/2tXKfYqExcvigkhx45B9+4weDD07q2zGJIh2Oz111/n1q1brFu3Tm/7fBqltdfFdw0/5NDw\nu3BtxeQrI9bD5/7zc2OgF3BFkqT1xdWj2LS2z8OygiVejl5su7hNbilGTXy8qPM+bZrcSpSPjY0N\nVlZWRY7d1NSUrKwssrOzycnJIS8vj/z8fABMTEwwVau3lZ4NG0S5wvh4kVp3xIhyPUlk9+7dBAUF\nKXcppUpp2A7UB9BoNK5AKNAAeEOj0XxT3EYMejxwfKvx/HT0J/o27ouJRjfPK0qKy+maJx3r22/D\nhAlixZLcKP1ctGrViuDgYIYPHw48iLmnpKRw9+5dUlJSiIuLIyoqipSUFNLT06lSpQp2dnZFaWwd\nHBwwMSnetax0ezwJrWlOSgITE6hYsVyvk7927RqzZ89mw4YNrFy5ks6dn99jLE67ZaEsMfnyOI/0\nGdSUJOniP+/HAKskSZqi0WgqAOFAsWadG7STH+UxisATgXx/+Hve831PbjlGR3y8WCp37ZrcSgwT\njUaDpaUllpaWTyzqkZ+fT1paGvHx8Vy+fJmTJ0+SlpZGvXr1ipy+vb19sZ1+ueKtt2DUKBF/f/11\nudXonZiYGL755hvWr1/Pq6++ysmTJ/WSyU5Frzwc6+kCfAcgSVKORqMpdnIIg43JF3It9RptFrfh\nRbcX+bTTp1hVstKhOt2jpNjfrFkiB31goNab1ipKsllZycjI4OrVq1y5coUrV66QnZ2Nu7s7LVq0\nwMHBQStL5ozGXvn58O238M03oiJeQAD06gWurlrflZJsFhQUxIQJE3j99deZOnUqVlbKu+eV1l4x\n7xl+TN71W63F5JcDt4CbiF67syRJWRqNpgawX5KkFsXRY/BOHiApM4lPgz9l3bl1fNzxY17zeU1n\nw/e6Rik3k4ICaNgQVq2Sb8lccVGKzXTB7du3OXPmDGfOnMHU1JTmzZvTokWLMhUUMTp73b0Lu3fD\ntm1is7QUzr5XL5Ecx6LsBa2UYrM9e/YwYsQItm/fTqtWrbTatjZRnbxWnHwlYCoiGc6vkiSd+uf3\n7QAXSZKWFUePUTj5QiITIxn39zi6OndlVrdZWtGj77icnDeTh481LAzGjIGoKOXEyZ52LpRyAwbd\nXS+SJHHjxg1Onz7N2bNn8fT0pKCgAH9//xK3pZRrTFs80qYkwalTYlnd1q1w+rRIfVvYy3d2LlW7\ncl9jhVpGjx6Nu7s7M2bM0Gq72iQ4OJjOnTuXMuPdbq1qkYNR/+umLSdvi8hsd/bh/9NoNO5AoiRJ\nScXRY5jd3afQzK4ZW0dt5c+zf7I6crXccgya3bvB3185Dr68o9FoqFOnDr1792by5Mncv3+foKAg\njh07Jrc0ZaHRiFmiH3wABw+K/PcvvSSW2rVpI9Lf/t//id6/ATJ8+HA2btwotwwV/TAXeFLSAyvg\n5+I2YlQ9+ULCboYxcPVALr15yeBqz8vdYyikTRv44gtRTU7pKMVmuiYjI4NLly4RExPDpUuXqFq1\nKi1btqRNmzYlaqe82OtfFBRARAR8/DH4+sKHHxb7o0qx2ZIlS1i6dCn79+/XarvaRs1dr5We/HFJ\nkryf9L8ajSZSkqRmxdFj0LPrn0brWq1p5diK3yJ+Y7LPZLnlGByXLonc9CVcjaOiAzIyMjh58iRR\nUVEkJyfToEEDXF1d6d69e5ni8uUSExPw8oI33xRrQ0vg5JXAnTt3+Oijj9i8ebPcUlT0w7My3RW7\nkLdRDdc/zGver7HsdLHmJTyT8pSHOTg4GEkSiW+mTdN+PfiyYgjnQhsa8/PzOX/+PH/++Sfz5s0j\nOTmZHj168O677zJ06FBatWpFtWrVDMIej6MLzSVqMzJSJH549VXttqtj9u3bx4QJExg5cqRWC9HI\nfj5UnkWMRqPp9fgvNRpNABBb3EaMsicP0LVBV4avG86drDtYV1ZzOReXefNEL37tWrmVlE/OnTvH\n9u3bqV69Oi1btmTQoEGypL41Ku7ehR07YPNmMRlv7lwYOVJuVSUiMjKS7du3ExcXJ7cUnXLk1Oii\n9y/UqcoLdcuUtl0vHL2WztHr6bpoehqwRaPRDEUkvwHwBtoCfYrbiFHG5Aup+2NdQsaGUK9GPa20\npw/kjP19/z3Mnw979pRoErLsKCVeWhYyMjLYunUriYmJ9OvXj7p16+psX8Zgr+cSEwMbNwrHfvy4\nmGHfp4/YnpCY6HnIbbO8vDxGjx5Neno669atw0ILywJ1iZq7vuwx+X8+WxEYCRTG388CKyVJyi6u\nHqPtyQOYmphSIBU7MVC5RZLg88/hzz9FSe5S3ANVykhYWBjnzp3Dx8cHc3NzJEkqn7Xiy0paGkyf\nDuvWwYABIjNe165QubLcysqEmZkZy5YtY+zYsdjb2+Pn50ePHj3o2bMnrq6uRnOtGMlhaA1Jku4D\nv5WlDaONyQOYaEzK7OSNPb4kSfDeexAUBF9/HaxoB28I56K0Grt06cJrr72GhYUFf/31F/Pnz2f/\n/v3cfcZSL0Owx+PoNAa8bx80awY5OXD+PCxcCH37ltrBK8m+wcHBmJubs3z5ci5dusTIkSMJDw+n\nU6dOuLi4MGnSJP7++2+ys4vdwStqVxdaVZSDUTt5S3NL0u6nyS1DseTlwfjxovceHAwKzI5ZrrC1\ntaVLly68+eab9O/fn8zMTBYtWsSqVau4ePEiBQXqqNQz+e47ePddWLwYataUW43OsLGxYfjw4fz2\n22/cuHGDjRs30rBhQ3788UecnJwYO3YsO3fuJC8vT26pKgrAqGPyfVb2YYLXBPo17qeV9vSBvmJ/\nWVkwfLjo9KxbJzKBGipyx0t1SU5ODpGRkRw/fpx79+7Rtm1bfHx8yjQ8a7T2mjYNqlSBmTO13rSh\n2OzmzZv89ddfrFq1imvXrjFy5Eg+++wzvS+3VNPaaicmrw2Muidft3pdrqWqJdQeJyVFJLmpVk3M\nTTJkB2/sVKhQgVatWjFhwgSGDBlCZGQkf/zxxzOH8cstEybA8uUi/lROe7G1atXirbfeIiwsjIMH\nD5Keno6XlxcnTpyQW5qKTBi1k69XvR5X714tUxvGFl+6eBE6dBBFZ5YuhYdXZyn9WJWuD3SrsVat\nWrz88ss0bNiQwMBAli0rex4IfaPTGLCbm5hJf+oU9OsnqtRpo10FUBotrq6uBAYG8tVXX9G1a1fC\nwsK00u7zUJLdVIzcydetXpdraWpPvpCgIFGR8/XX4YcfRAIwFcPCxMQEX19fRowYweHDhzl58qTc\nkpSFjY1YC5+RAT/+KLcaRdCiRQvMzMxwcXGRW4qKDBh1TH5P7B5mhsxk35h9WmlPH+gq9nfwIAwb\nBhs2gI9PmSQqDkOJl2qb+Ph4Fi1axLRp06hevXqxP1cu7BUTA82bC2evhadZQ7SZJEkEBQUxffp0\nJk+ezNtvv623fasxeeXE5I16nfzV1KvUra67pCKGxJYtYia9sTn48kxkZCTu7u4lcvDlhqwsqFOn\n3A5XBQcH8/7773P//n3mzp1Lz5495ZakIhNG/Q0IOhdEa6fWZWrDWOJLu3eLnCDPQunHqnR9oB+N\nOTk5bNq0ib///puAgACd70+b6CUGnJ4uZtr37avddmWkuFquXLnC4MGDGTt2LFOnTuXEiRP4+/s/\ndTWGGpM3fozWyW++sJkLdy4wvtV4uaXITnIyREeL8rEqhk1CQgILFiygoKCAfv36YakujXiUa9eg\nSxdo2BBmz5Zbjd7Iy8vj888/x8vLi5YtWxIVFcXIkSMxKacjGSoPMMqY/MFrBxm0ehBrXlyDX30/\nLSnTD7qI/SUkQJMmkJQEZkYYoDHEeGlpuXr1KkFBQTg6OtK5c2fs7e1L3IZR2isjA775Bv73P5EQ\n5/33tZojVck2y8rKYsSIEWRkZPD7779Tp04dne/zeagxeeXE5I3uMS/0eiiDVg9i5eCVBufgdYW9\nvSg4s89w5h+qPIV69eoxZcoU6tevz7JlywgKCiI5OVluWfJx754ondi4MVy9CidPitz15SgJeu/e\nvbl//z7btm1ThINXURZG5eQvJV9i0F+DWDpwKd0adNNKm8YSX5o+Hd54AzIzn/4/Sj9WpesD/Wg0\nMzOjTZs2NG/eHBsbGxYvXsymTZtIS1N+Cmet2Sc9XaSxdXEheMUKsWxk2TIx2U5LKOl6e5aWkSNH\nEhERwZtvvkliYqLW2i0tSrKbihE5+Yt3LuK/wp+PO36Mv6u/3HIUx9Ch8MIL8NFHcitR0RYVKlSg\nY8eOTJkyBY1Gw5w5c0hJSZFblu5ITBQZ7UaPFkNTJ07A9u3w9dfletnIq6++yvnz56lcuTJNmzZl\nwIABfPvttxw8eLDEBWtUjA+jiMnvvbyXEetG8EWnL5joPVFHyvSDLmN/iYnQtClERIAOy5XrHSXH\nS/VBWloaK1asoEGDBvTo0eO5ee0Nxl45OXDkCOzYIZz5pUtiUp2/PwQEaLXX/jwMxWZxcXGEhIRw\n6NAhDh8+zLlz52jevDm+vr60a9eO9u3bY2dnp3MdakxeOTF5g3fyd7Lu4DrXlXVD19HFuYsOlekH\nXd9MPvhATMALDCyVPEViKDdgbZGTk8P169e5cuUKV65cISEhgY4dO+Lr61uswjWKtFdBgVgCcuwY\nhIWJ18hIMWPU319sbdqAubnuNDwDRdqsGGRmZhIWFsbhw4c5fPgwoaGhfPLJJ0ydOlWnNehVJ68c\nJ2/wc63XnVtH9wbddebgg4OD6dSpk07aloN33xWri957T7w+jNKPVen6QHca4+PjiYqKKnLqjo6O\n3L17lwEDBlC7dm3MZXJ+JaXIPnl5sHkzhIYKp37ihEhJ6+MjtmHDoGVLUVWuuG3qSqsCKK0WS0tL\nOnfuTOfOwsnExsYyfPhw9uzZw4oVKzhx4oTWj1GNySsLg3fyV+5eoZF1I7llGAw1a8Kbb4ow5m+/\nya1GpbjExMRw6tQp0tPTcXV1pUGDBty4cYO6detiamoqt7ziI0liktyMGWBtDT17iuVu3t7Cyavo\njMzMTIKDg8nPz+fMmTNcv35dbkkqesDgh+v3Xt7L/+38P469egwzE4N/ZtHLsODdu+DiIjpP9eqV\nWKLiMNSh1JIiSRKpqalcv36da9eucf36dZKTk3FycqJ27do4ODhgZ2eHtbX1Mx2/bPZKSIDBgyEt\nTSSq8fc3mKVuhnqNpaWlERISwrZt21i1ahW+vr5MmjSJnj176vThUB2uV4frtUaHuh1wrOJIn5V9\n+OvFv6hWsZrckhRPjRrw4ouwZg28847calSKi0ajoUaNGtSoUQMPDw8AsrOzuXHjBjdu3CAqKorg\n4GBSU1OxsrLCzs4OOzs77O3tsbOzo3r16jqNwz4XMzOwsICUFPHeQBy8IZGens7BgwcJDg5m3759\nREVF0bp1a7p27UpERAR1jWnGrUqxMHgnb25qzsYRG3lz25u0W9KOZQOX0dKxpdbaV1JcTpsEBMDc\nuY86eaUfq9L1gX41Fu7L1dUVV1fXot/n5uaSlJREYmIiiYmJhIWFcevWLXJzc3F0dNSLtidibU3w\nhx/SKS0NJk2CRo1E8gZ/fyhDr7I8x+QLCgo4ceIEW7ZsYdu2bURGRuLt7U3nzp35/vvvad26NRYW\nFiVut6xaVZSDwTt5ADMTM+b1msey08vwX+HPOM9xfOL3CZXMK8ktTbF07CiWG+flGWeq2/KMubk5\nTk5OODk5kZaWRnh4eNFkvdaty1awqcxoNNC/v3Dsy5fDl1/C5Mnwyiswbpxel8UZKunp6ezatYst\nW7awdetWqlevTu/evfnqq69o164dlSqp9z2VBxh8TP5xEjISmLJtCqcSTrG472I61OugRXW6R5+x\nv4YN4e+/wc2txB9VFIYaL9UliYmJHDx4kIsXL+Lh4YG3t3fR+mjF2evUKbGmc+VKkcihZ0/xEODl\nVaYevjZRgs0yMjKYOXMm8+fPp02bNvTu3ZvevXs/MoqjFNSYvBqT1xn2Vez568W/2HB+AyPWjaBf\n437M7jabqhWryi1NcVhbi0l4KsZDfHw8Bw4c4Pr167Rp04ZevXo9c7hWEbRoAb/8At9/DyEhIvHN\n2LFiol737sLh9+0LVlZyK5WN1atX884779CpUyfOnz+Pk5OT3JJUDASjSWv7OAOaDCDytUhSslMY\n+/fYUrdjzPGlKlUezWWv9GNVuj7Qr8bH93Xz5k2WL19O/fr1mTp1Ku3bt1ecg3+mfSwshFP/4Qc4\ne1akZuzaVQw3eXjAwYMlb1NXWvVIeno6I0aMIDAwkGXLlmnVwau5640fo3XyADUsavBrv185EX+C\nXZd2yS1HcVhaPrtgjYrhkJ2dzdq1a+nTpw8vvPCCwSTHeSZ16sD48RAUJIbzBw+GhQvlVqV3qlat\nStu2bYmNjZVbiooBYnQx+Scx5+gcziaeZWFf5d8g9Bn78/CABQvA17fEH1UUSoiXyk18fDzr1q3j\njTfeeO7/Gqy9CnPX//ADDBmi113LbbPw8HD69OnDq6++yieffIKZwmfLqjF55cTkjbonX0j9GvW5\nlnZNbhmK4sQJSE6Gtm3lVqKiDWxsbEhNTSUnJ0duKbrDxQXWrxez8cPC5FajV7y8vIiIiODo0aN0\n7NjRIMoKqzxAql/w3E1XlAsnv/fyXlrYtyjVZ40xvnT9OgwcKJKOmTx0BSj9WJWuD+SLyZubm+Ph\n4cGWLVuQvcf9DMpsn1at4PffoU8f2L9fO20+BSVdb8HBwTg4OLBt2zbc3NyYNGmSVs6zGpM3foze\nyZ+/fZ5lp5fxRuvnD2OWB+7cgR49YOpUsU5exXgICAggPj6ebdu2GXcd8d69YdUqMWT/7beiJG05\nwcTEhDlz5nD69GmCgoLklqNiABh1TD42JRa/3/2Y2XkmYzzHaKVNXaPL2F9GBnTrBp06wTfflFah\n8pA7XqokMjMz2bNnDxcvXqRLly54enr+K5Wt0djrwgWRsjEyUjj7wYN1lipXaTZbt24dc+bMYf8/\noxlKQ43JPxqTzzjp99zPVfHcr8bkS8KNtBt0W9qNGe1nGIyD1yV37ojlxu7uMGuW3GpUdIWlpSV9\n+/alVatWbNy4kStXrsgtSXc0agQbN4qZ959/LtbW5+bKrUov9OvXjwsXLnDu3Dm5pagoHKN08gkZ\nCXRb2o3XfF7jNZ/XytSWMcSXLl2Cdu3ELPrAwKd3dpR+rErXB/KukwdISUlh5cqVREVF8dJLL+Hs\n7Kw3PcVBJzFgU1M4cgQSE8Vkk3v3tNOugq63x7WYm5szbtw4FpZxSaEakzd+jM7J38m6Q/dl3RnR\nbATvtFNLrG3bJhz8tGn/nminYlykpaXx+++/U69ePSZNmkSDBg3klqQ/LC1F0pxKleC1sj3YGwqd\nOnXixIkTcstQUThGFZPPzsumw28d6Fy/M7O7zZa3rGYp0VbsLycHZs6EJUtg9Wpo316rMhWF0uKl\ncpCfn8/ixYtxd3en/XNOtlHbKzMTWrcWT7Wvvqq1ZpVosx9//JGLFy8yf/58ne2jtKgxeeXE5JWd\nUaGEHLlxBEmSDNbBa4t9+0RnxsUFjh8HOauLquiH7OxsUlJS8DX0zEZlxdJSZMjr3Fm8HzlSbkU6\n4datW3z33XesXr1abikqCseoBm8jEyOpVrEaWblZWmvTkOJLGRmiWueYMfD117BpU8kcvNKPVen6\nQL6YfKVKlcjJyaGgQHdJNbSBXmLAjRvDrl1i5v2yZdprV0Ye1pKcnMzQoUMZP348HTqUrcqmGpM3\nfozKyQ91H4pDFQeazGvCyjMrFZ0URNucPi0qcwJERYn5R+V4MKPcYWJiQuXKlcnK0t4DrkHj7g67\nd8OHH8LPP8utRmscO3aMVq1a4ePjwyeffCK3HBUDwKhi8oUcunaIqdunYqIx4VO/T+nVsJfBDN+X\nNpZlbS3x44/w0ku6UqZclBgvlYPAwEBatGhB69atn/l/5cpeV6+K7E9t2sB334GdXamakdtmmZmZ\nzJo1i0WLFrFgwQIGDRqklXZ1hRqTV05M3qh68oX41vUl7NUw3vN9j+l7ptN6cWs2RW8iJ994M2O9\n/375dPAqDxg0aBChoaEEBwcrftheb9SrJyam2NqK3v28eQa3ln7t2rW4ubkRGxtLRESE4h28irIw\nSicPYKIxYYjbEE5NOsV03+l8eeBLas6uSdslbZm6bSorz6wkJjnmuUP6hhJfKkbxseei9GNVuj6Q\nd528tbU1r7zyCpcvX+ann35i165dJCYm6k1PcZAlBly1Knz/vZiRun491KoFb74Jx47BM77/Srje\nFi9ezPTp03nrrbdYuXIltWrV0mr7akze+DGq2fVPwkRjwmC3wQx2G0xGTgbhceEcvXmUoHNBvL/7\nfbJys2hdqzWtnVrTyrEV7nbuONdwxtTEVG7pJaJSJbkVqCiBKlWqMHbsWBISEjh9+jTLly/H0tKS\nFi1a8MILLxhM2EonNGsm4vQxMbB8OYwYAWZmYghsCG3lTwAAIABJREFU2jQxG19BREVFMWPGDA4c\nOEBCQoLcclQMFKOMyZeEuPQ4wm6GcfTGUU4lnOJs0lmSMpNobNMYN1s33G3di14b1Gygc+cvd+zP\nEFFt9nQKCgqIjo5m3bp1TJ8+HTMzM9VehUiSyJT37beQlgabNz/1aVkOm/3000+cP3+eBQsWlLoN\nuVBj8sqJyRt9T/55OFV1YkCTAQxoMqDod+n30zl3+xxRSVGcTTxL4IlAziaeJTEzkUbWjXC3c8fN\nxk282rrhUtNF9p5/WhpUqyarBBUFUlBQQHx8PC4uLpiZlfuv+6NoNNC2LaxdC//5j1h7+tdfcqsq\nwt7enuXLl7N//37atm1LhQoV5JakYoAYbUy+LFStWJXWtVrzsufL9K7Qmy0jt3Bl2hUS300ksG8g\nPRr0IPV+KotPLKbn8p5UmVWFFgtaMHLdSGYemMn6c+uJvh1NXkGe3jT7+UF8fNnaUHosTen6QP7c\n9YXk5ORw+PBh5syZQ1xcHF27dtWbrmehyBiwqakoz5iert12y0i/fv3o1asX7777LlZWVgwcOJCF\nCxdqteiQIs+HilZRH+1LQJUKVfCp5YNPLZ9Hfp+Zk/lIz39JxBKikqKIz4insXVj2tZuS4d6HehQ\ntwN1qtfRibYhQ0SO+m3boEkTnexCRcFkZ2cTHx9PXFwccXFxXLlyBWdnZ0aMGIGjmvLw6WRmitr0\nn38Ov/0mt5pHsLS05IsvvuCLL75gw4YNZGZmsn37dj7++GNsbW159913GT16tDpCo/JMyn1MXpdk\n5mQSmRjJoeuHCLkWwsFrB7E0t6R93fZ0qNuBDvU60NSm6SOTocoS+/v9d5g+XWT1bNdO20ejXMpb\njDk3N/cRhx4XF0daWhoODg44OTnh5ORE3bp1qVGjxhM/X97s9USiomDBAlixQhR2eP11sZ7+KSjJ\nZgUFBezfv59PP/2UxMREPv/8c1588UVMFFR9So3JKycmrzp5PSJJEtF3ogm5GsLB6wc5cPUAZiZm\nvO7zOi97vkwNixplvpls3w6jRoGPDwwbBgMGQM2aOjkcxaCkG7CuOXfuHJs2baJmzZpFDt3JyQlb\nW9ti3+TLk72KkCQ4eRI2bBDV6hIT4ZVXRBGbunWf+3El2kySJHbt2sXHH39MdHQ0LVq0wNPTs2hz\nc3OjYsWKOtv/s1CdvOrkn4rSbibBwcF06tRJJ21LkkTojVB+CfuFbTHbGOo2lEX9FpX5ZpKZKSYK\nr14Ne/ZAhw4wdCj07g3W1k9vR5fHqg2epk9JN2Bt21CSJLKyssjIyODUqVNERUUxdOhQnJycSr0v\nOe2li2vsiW3m58PNm3DuHGzdKpy7ubl46h0wQEy4M332ZNmH25X7Gnue3W7fvs2pU6c4efJk0Xbp\n0iUaNmxY5PRbtmyJj48Plg8tFdTV+ejcuXOp7LVi8m6tapGDUf/rpignrwZzZCT5XjLx6fFYVbKi\naoWqrD6rnYpSlpaiFz9smJh1v2mTmDT8xhvg5gb+/mLz8XnufU5FR+Tl5ZGenk5GRkbR9vjPGRkZ\nZGZmUrFiRapWrYqDgwMTJkygcuXKcstXBnfvQmws7N8vEtvExj7Yrl8XT7SurmIYfutWcfEbaZ4A\nGxsbunbt+sgEy+zsbM6ePVvk9NesWcPp06dxd3enQ4cOZS5uo2IYqD15PZFyL4XoO9FcuHOBiPgI\n9l3ZR2xKLO3rtqdz/c50du5MS4eWmJma6azHcP8+HDwohvR37BAdne7doV8/6N9fcblAio3cvayn\nkZuby5kzZ0hKSvqXM8/Ly6NKlSpP3KpWrfrIz6ZafhJTqr2eSkqKqCoXHi4c+OXL4jU3Fxo0eLA5\nOz94X6+eVjNEGZzNnsK9e/cICwsjJCSEkJAQQkNDqVOnDh06dKBnz574+/tTSQt2K629VkzeU+Z9\ny82o/3VVVE9edfJaJCc/h9iUWKJvRxc59Og70UTfjuZe3j0aWzemsU1jmtk2o1P9Tng7eWNuav5I\nG/q8mdy4IZz9unUQGioc/X/+A506gYLm8DwXpd2Ac3NzOXHiBIcOHcLR0ZG6des+4rirVq2KhYWF\nbNnnlGavf1FQACdOiKUi27fDmTPQsaOYTeri8sCZW1vrrWeueJuVkry8PE6dOsWBAwfYvHkzx48f\np2fPngwZMoRevXpRpUqVUrWrOnnVyT8VpX0xnhWzikmOYdvFbey5vIeopCiupV6jdrXaNLZpLBy6\ndWMaWTeisU1jHKs4FuumLtfN5NYt+OKLYA4f7sSdOzB4sOjl+/lBKb/nWscQYvIbNmzg0qVLODg4\n0KVLF50uXzOqmLwkiSH3hQtFfMnaGgICRFypY0ewsCh5m1rUKvc1po9jBEhKSuLvv/9m7dq1HD58\nmD59+jBv3jxqlmD2btli8qqT17YeNSZfArLzstl/ZT9bL25lW8w20nPS6eXaixHNRuBh74FLTRcq\nmskzm7WsODiIyXnz54va9Js3i8qcw4aJ2H337iK02bKlGsd/FpUrV6ZWrVqkp6dj8QzHpPIPWVnw\n55/iwktOhokTISwM6teXW1m5xNbWlvHjxzN+/HhSUlL47LPPaNeuHVu2bKFBgwZyy1MpBWpPvhik\n3U/j65Cv+d/x/+Fh50Gvhr0IcA3A08FT60OucvcYHicjQ8xr2rlThEUTEsDXVywt9vUFb2+QaZVO\nEUqzmSRJHD16lJCQEHr16oW7u7tO9lNaFGGv+/dF2ddZs0St98mToWdPxT5BKsJmMjF69GgiIiI4\ne/ZssT9TWns1/yr4/9s77/gqyqzxf08oigUCQlBUQA1K7yJtF1FAQJqCgiuCIogurtjWF2VXX3XX\ngmtoyiKuoCj1hyzgS1OaISIgJYQiRXqJtIUFgQAh5/fHM8FLTLlJ5t47997n+/ncT2YmM2fOnHlm\nzjztnPyq5zlSBt9pa/LhQnpGOp+s+YTXlrxGuyrt2PTHTVxf0t1Uj17nqqvM1Lt77zXr+/fDd9+Z\n3zPPwJYtULfur06/adPcp+lFAyJC48aNqVixItOmTWPnzp3cc889FCtWLO+DIx1Vk+71pZdMaMbE\nRKhWLdRaWbIhPT2dwYMHs3TpUqZMcWfmj6VwiEhp4Hh+vh5tTT4bfjn3C2PXjmXo8qGUSi3F2IFj\nqX9d/aCc25P9pblw8iSsWGGcflKSWb7hhl+dfvPmZoyUGw0e4dAnn1XHtLQ0Jk2aRM2aNbn99ttz\nPtCFc/lLyOx19ChLunThzmPHYOhQ0wfkArZP3n25iYmJPP3009xwww2MHz+esmXL5ktmQfvkt0VA\nMJwqLgXDEZFXgamqullELgPmAXWAdOAPqupXUAFbk/fh8KnDDF0+lDGrx9DyppZM6jqJtJ/Sgubg\nw5Grrza5PVq1Muvp6WYwdFKSGRw9eLDZlun0O3SAKlVCq3Mwufzyyylbtixnz54NtSqhQ9XMU+/f\n3xSCBQtC38djyZbNmzfzxhtvkJSUREJCAl27dg3ZLBAL3YE3neXezt9ywK3AZ4BfTt7W5DGpZRO+\nT2DEyhF0r9GdF5q8wC1lbgmqDpmEusbgNqqwZ49x+kuXmpbaW26BRx4xg/rKlCn8Obxus5SUFBYs\nWECJEiWoUaMGNWrU4JoQ9mkEzV4XLphECu++awbYDR/uWu092Hi9jBWG/fv3M3nyZCZOnEhqair9\n+vXjpZdeuiQyXn6xYW1dqcmvVdV6zvKXwNeq+pGzvkZV/ap95unknWaCRKA4puY/TVVfF5EBwLPA\nzUA5Vf2Ps38LYCawwxExXVX/5vyvLTAMk+L2E1V9N5vzBfXBmLNtDn1m9qH1La15/c7Xubl0aEeQ\nRvLLBEz8kq+/hvHjzRToNm1M8q/CTNMLB5upKnv37mXDhg1s2rSJkiVL0rp1a2666aag6ZBJUOy1\nYgX07Ally5qsSR07hlfwhSyEQxnLLzt27KBv376sXbuW++67j4cffpg777zTleBL1sm74uSXA32B\ng8AWoIGq7nT+t1lV/co3mudTp6pngZbOF0VdoJ2INAKSgLuB3dkclqiq9Z1fpoOPAT4A7gFqAA+J\nSMiTok7bNI1BzQfx+X2fZ+vgoyk3cjCutVgxM4hvyhTYvdtM1/vxR/+ODYd7kZOOIkLFihVp3749\nzz//PPHx8WzYsCEg5/IEX31lHPuyZSbKkuPgwyl/uZfsGwhdFi1aRFpaGgcOHGDs2LHcfffdrjh4\nL9ktzHkWmAZsBob6OPj2wFp/hfj1aa2qp53FyzC1eVXVdaq6B8juay27bY2Abaq6W1XPA5OBzv4q\nGghUlb0n9lKmhAttxpZ8ExtrfqdP571vJBETE0Pp0qXZt28f27dvx8u1vQJx+rTJ+Fa+fMTGio8E\nRISSJUu6EsbW4j6qulxVq6rqNar6ps/2Oar6kL9y/HLyIhIjImuBn4FvVPWHPA5pLCJrRWS2iFR3\ntl0P7PXZZ5+zLSSoKs/Oe5Zfzv3CfVXvy3E/L2dlc5tQXGuXLvDxx/7tGw73wl8da9WqRcOGDVmw\nYAEjRowgMTGREydOBORcQWXFChMxqWRJM9AuC4HQOVB28JJ9A6FLlSpVOH78uOtyvWS3cEZEOopI\nJZ/1V0VknYjMEhG/+/n8Gl2vqhlAPREpCcwQkeqquimH3VcDlVT1tIi0A2ZgRgNm90mfbRXm0Ucf\npbIT8So2Npa6deteLDiZTUGFXf++yPck7U3i9cqvs/r71a7L93d92LBhJCcnX7zeghIMmwVifcAA\nqFhxCf/6F/Tt69/xkWCzpKQkAPr3709qaiqffvopX3zxBUOGDCE2NtbV8y1ZsoRPP/0UoFA2y9Ne\nO3dy55//DKNGsSQuDpKTPVHGCrIeCWUsr/Vdu3axefPmi7p6oYzdX+r9Ah/rHe50S9DfgcYAItIB\n6Ak8BNQDRmO6vvMk36Prnbl7v6hqgrO+A2iYOfAum/13Ag0wjv5/VbWts30Qptn/3Sz7B3ywytxt\nc+n7VV9+6PcDFa6ukOu+S4KcYz3c5sm7wSefwOjRsHx57gHPctLPS4OiCmPDqVOnUrVqVWrXrh3Q\ncwXMXv37Q4UK8NprOe4SiDIWqHLrKzfUZczta1RV+vTpQ3p6Op9//rlrcqFw8+RtxLtLjlunqnWc\n5bHAlkx/mZ/R9Xk214tIWREp5SyXAFphBgJc3AWfWrqIlPdZboT5kPgP8AMQLyKVRKQ40AOY5Y+S\nbnLk9BEem/kYU7pNydPBW4JDnz4m731ycqg1CR3nz5/n2LFjZGRkhFqVgrNhA9SqFWotLLmgqsye\nPZtmzZqxcuVKunfvHmqVLkUk/H9uWkPkKmfQ+t2Ab/YevxNj+DOFrhZm4n2M85uiqn8XkT8BLwHl\ngUPAHFV9wpla9xRwHjgDPKeqKxxZbYHh/DqF7p1szhfQmnyfmX2IvTyWhHsSAnaOwhDqGkOoaNgQ\n/vlPkwwnv4S7zVSVadOmUbRoUbp06RLw4CMBs9fkyZCQALNnQ7lyhVHRc4R7GTt06BBz585l+PDh\npKen85e//IWuXbu6Mpo+Owocu/6tbwOiTzBJeaWFWzX5PsArwAngkE8reD3gH6p6tz/65Nknr6rr\ngd80C6jqSGBkNts/BD7MQdY84DZ/FAsUZy+cJUPDuLYUgaxfbwLmRGuSq8WLF3Py5El69eoVcAcf\nUB54wMSiv/VWM6LymWfMIDxL0Dl37hzff/898+fPZ968eezYsYO77rqL1157jY4dOxIT482YBcuq\n/W+oVSg0bmXmVtWxIjIfiAPW+fzrZ+Axf+V4804HkJHtRjJj8wxmbfGvpyBzcEk0EIprVYUBA+DN\nN/NObBMO9yK/Om7fvp2UlBS6d+9O0aL5izLtOXsUKWJSxm7bBrfdBp06we9+B8OGwaZNoBoQnQNl\nBy/ZNy9dTpw4weLFi3n33Xfp3LkzcXFxvPjii8TExDBixAgOHz7M9OnT6dy58yUOPpzuRzSiqvtV\nda0z+D1zW6ozfd0voi52fZkSZZjUdRJdpnShTvk6VIqtlPdBloCxbBmkpkLfvqHWJDRcddVVnD9/\nPtRquEtmlLsXXzRN97Nnm4Q0qqbP/vBhk+ygdOlQaxqWnD9/npSUFFauXHnxt2vXLurWrUujRo14\n6KGH+Pjjj4mLiwu1qvkmnBuyvErUxq4f8t0Q5v40l0W9FnmqiTTc+/7yS5cu5n3/9NMFlxHuNvvm\nm2/YsmULNWrUoEqVKlSoUCGgzakhsZeqyUv89dcwf75JZFC9uskhf8890KgR5LMlI5iEuoypKsuW\nLSMhIYF58+Zx0003cccdd9CoUSMaNWpEzZo1PZXKuKD2OjWjZaBUChpXdlnsqXzyUevkL2RcoPbo\n2gxpNYR7b7034Ofzl1C/TILJypXQtSts3QqFCboV7jbLyMhgz549bNu2jW3btnHq1Cni4+OJj4/n\n5ptvLlSikOzwhL3OnjVZi+bPN45/9264665fnX4lb7Wwhcpm6enpTJs2jaFDh3L06FGee+45evbs\nSalSpQolN9BYJ+8dJx91ffKZFIkpwoPVH2T6j9Nz3S+a+peCea2q8MILZkq1vw4+HO5FQXSMiYmh\ncuXKtG7dmj/+8Y/069ePG2+8kQ0bNjBixAjef/99Pv/8c+bNm8eaNWvYu3cvZ8+eDQt7ZOWizpdd\nBnffDUOGmLmTmzaZGPfffmumWtxxh+nLP3DAf5mB0jUEpKWlMXr0aKpUqcKoUaPo2LEjW7ZsYcCA\nAa46eNsnH/l4t30swJw6d4oxa8Yws8fMUKsSlYwfD2lp8JjfY0Sjh9jYWBo2bEjDhg1RVU6cOMGh\nQ4c4dOgQe/bsYdWqVRw5coQDBw6wf/9+4uLiiIuL49Zbbw3fOOTXXQe9eplfejosXAiTJsHrr0Pd\nuvDww6awBGjKl1dQVYYPH857771HvXr1mDBhAk2bNmXJkiUBm+7mJVKX5S+0syVvorK5/mz6We6b\nch/XX309H3fyM3B6kPBEU2qAOXbMdMd+9ZWptBWWaLBZVjIyMjh+/DiHDh3i8OHDpKamsnPnTurU\nqUOTJk1yre2Flb3S0mDuXHjrLdOM/7e/BV8Hgmez06dP06JFCw4cOMBbb73FQw89RPHixfMlwwvY\nVLPeaa6POie/78Q+npr9FMWLFGdKtykUjfFWY0ZYvYALyNNPw4ULJviNG0SDzfzhxIkTzJ8/n82b\nN1O3bl06dOiQ7aDSsLTXgQNQtSp8+SW0bh300wfTZqrKwoULeeedd9iyZQtPPPEEbdq0oUGDBvme\nZhkqrJP3jpOPmj75vf/dy4DZA6j9z9pUvaYqk7pO8svBR1P/UjCudf9+mDjRzIvPL+FwL4Kp4/z5\n89m+fTuJiYlMmjSJMWPGsHv3buLj4ylbtmzQ9MgP+bbP6dPwwQfQrBnUrw/XXlt4mX4SqvImIrRq\n1YoFCxYwY8YMjh49So8ePShXrhydO3dm5MiRbNq0yZUUxbZPPvIJj8/CQrDnv3t4e+nbTNk4hb71\n+7L56c3EXRl+80cjhQ8/hEceMVOpLflHVVm+fDmrVq0iJSWF5s2bU6FCBerUqUP79u0pWbKkp6aE\nFpj16+Hzz+Gzz4yDnzzZDMaLMho0aECDBg3o0qUL1apVY9GiRSxcuJCEhATS0tJo0aIFzZo1o2nT\nptSuXdtT0+gs3iBim+t3H9/N20lvM3XjVPrV78eLTV+k3JXej6cdlk2p+aB2bRgzBho3dk9mpNss\nk+PHjzNz5kwyMjJo27Yt5cuXL9B8es/a6+efzWC78ePh6FHo2RMefdSEyQ0xXrTZjh07WLp0KcuW\nLWPZsmXs2rWLhg0b0rRpU5o2bUqTJk0oU6ZMwM6fG7a53jvN9RHp5Pef2E/Nf9bkyQZP8kLTFyh7\nRfhUG734MnGLo0dNfPqjR92NexLJNgM4duwYK1asICUl5eILvDDBcjxlrzNnYNYs49iXLTPRkXr1\nghYtwEPx1T1lsxw4fvw4y5cvv+j0V65cSbVq1ejatStdu3bllltuCZou1sl7x8l75ylykWV7l/H7\nSr/n7VZvF9rBR1P/UqCvdfVq061aUAcfDvfCTR3379/P1KlT+fjjjylatChPPvkkzZs3v+jgw8Ee\nWbmo83ffmVjG118PY8fCH/4A+/bBuHHQsmW+HHyk9clnhz+6xMbG0rZtW9544w0WLFjA0aNHefPN\nN9m+fTtNmjShXr16/P3vf2f37t35khsIXS3BIyKdfIliJVi2dxlDvhvCmfNnQq2OxWHdOjPl2ZI3\nJ0+e5IsvvqBy5co8++yztGrVipIlS4ZarcKTkQGvvGKc+m23mb73+fPNPHiXI/tFO8WKFaNNmzZ8\n9NFHpKamMnToUHbt2kWHDh1CrZoliERkcz3A5iObeWXhK6w6sIr3Wr9H95rdXdAu8IRDs2BBefxx\nM3bqiSfclRuJNktMTOS///0vHTt2dF12yOylavrZd+6EmTPDKud8pJSx9PR0SpUqxf79+4mNjQ3Y\neWxzvW2uDzhVy1ZlevfpTO42mRe/eZFJ6yeFWqWoZ/NmU3mz5M2WLVuoXbt2qNVwlwsX4OBBs3z8\neGh1iUIyMjIYPHgwFStWDMsAO5aCEbFOPpOmNzZlzh/mMHDeQBbtXJTv46OpfymQ16paeCcfDvfC\nDR3T09M5ePAg1113XcD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            "text/plain": [
              "<matplotlib.figure.Figure at 0x1155054e0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DoSL8oJsXzeo"
      },
      "source": [
        "Finally we can write out a csv file with the well data along with the facies classification results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNCMXkyJXzeo"
      },
      "source": [
        "well_data.to_csv('well_data_with_facies.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TAadQ4RXzer"
      },
      "source": [
        "## References\n",
        "\n",
        "Amato del Monte, A., 2015. Seismic Petrophysics: Part 1, *The Leading Edge*, 34 (4). [doi:10.1190/tle34040440.1](http://dx.doi.org/10.1190/tle34040440.1)\n",
        "\n",
        "Bohling, G. C., and M. K. Dubois, 2003. An Integrated Application of Neural Network and Markov Chain Techniques to Prediction of Lithofacies from Well Logs, *KGS Open-File Report* 2003-50, 6 pp. [pdf](http://www.kgs.ku.edu/PRS/publication/2003/ofr2003-50.pdf)\n",
        "\n",
        "Dubois, M. K., G. C. Bohling, and S. Chakrabarti, 2007, Comparison of four approaches to a rock facies classification problem, *Computers & Geosciences*, 33 (5), 599-617 pp. [doi:10.1016/j.cageo.2006.08.011](http://dx.doi.org/10.1016/j.cageo.2006.08.011)"
      ]
    }
  ]
}